Validation / Papers / Mahbod 2024
Mahbod 2024: NuInsSeg, nuclei instance segmentation in H&E images
How to read this page
In this validation, a script plays the scientist. It gives the answers that we wrote before the run, from the methods of the paper. The run is one sample: another run can give different steps and numbers. The model is the AI. The harness is Cuvette, the software around the model: it runs the programs and records each step. A tool call is a request from the model to run one program step. The session record is the log of each message and each step. The claim check is a script that finds each number of the final answer in the step results. The review is a set of fixed rule checks plus a second AI model, the referee, that reads the record. A deviation is a request from the model for a setting that differs from the choice of the scientist. Each Claude model did 3 runs of this paper. This page shows run 3 of each Claude model and the one run of qwen3:8b. The table of values says how many of the Claude runs match.
Opus: 3 of 3 values match, 1 of 1 correct in the final answer. All 3 runs: 3 of 3 values match. Sonnet: 3 of 3 values match, 1 of 1 correct in the final answer. All 3 runs: 3 of 3 values match. Haiku: 3 of 3 values match, 1 of 1 correct in the final answer. All 3 runs: 3 of 3 values match. qwen3:8b: 3 of 3 values match, 1 of 1 correct in the final answer.
The figure in the paper and in the run
As published
Reproduced in Cuvette
The paper
Mahbod A, Polak C, Feldmann K, Khan R, Gelles K, Dorffner G, Woitek R, Hatamikia S, Ellinger I. NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images. Scientific Data 11:295 (2024). doi:10.1038/s41597-024-03117-2
Related sources:
- Mahbod A et al. NuInsSeg dataset, Zenodo record 10518968. Source of the images and the hand-drawn masks. doi:10.5281/zenodo.10518968
What it measured
The paper releases 665 image patches of hematoxylin and eosin (H&E) stained tissue from 31 human and mouse organs. The authors outlined every nucleus by hand in ImageJ. They also marked vague areas where a precise outline is not possible. The paper trains six U-Net models with five-fold cross-validation. It reports the Dice score, the aggregated Jaccard index and the panoptic quality as baselines for other methods.
Data
NuInsSeg dataset on Zenodo (record 10518968). Size: 1.63 GB zip. We keep 1.6 GB of images and masks: 665 RGB patches of 512 x 512 pixels, 31 organ folders..
License: CC BY 4.0 (Zenodo record). Human and mouse tissue patches with no patient identifiers.
The instruction
A script sent this message as the scientist. The file paths point to the fetched data.
ScientistOutline the nuclei in these H&E patches and tell me how close the outlines are to the hand-drawn ones. Use all organ folders. The patches are in the folder '{data}/mahbod2024-nuinsseg/x'. Each organ folder has a 'tissue images' folder with PNG files and a 'label masks' folder with the hand-drawn masks.
The same request in the words of the paper's method:
Here are H&E patches from many organs, with hand-drawn nucleus masks for each patch. Outline the nuclei automatically in all the organ folders. Tell me how close the automatic outlines are to the hand-drawn ones.
Basis: The Technical Validation section of the paper. The paper compares automatic nucleus segmentation with the hand-drawn masks. We ask for a classical threshold method, not a U-Net.
Results
Match: a number in the session record is inside the tolerance of the known value. In the final answer: the model also stated the value in its final answer. For a Claude model, each cell shows the run that this page shows. If the three runs differ, the cell also says in how many runs the value matches.
| Value | Known value | Tolerance | Opus | Sonnet | Haiku | qwen3:8b |
|---|---|---|---|---|---|---|
mean_dice_otsuMean pixel Dice, Otsu on the hematoxylin channel.Source of the known valueWe calculated it with scikit-image 0.26.0Not in the paper. The paper reports Dice only for U-Net models (0.766 to 0.814 in Table 3). We scored a plain Otsu threshold on the hematoxylin channel over all 665 patches. | 0.538 | ± 0.02 | 0.53805 matchIn the final answer: yes (0.538)Log: n10 run_script stdout, entry 80; the final answer, entry 166 | 0.5380504 matchIn the final answer: yes (0.538)Log: n12 count_objects metrics.mean_dice, entry 65; the final answer, entry 148 | 0.5380504 matchIn the final answer: yes (0.538)Log: n12 count_objects metrics.mean_dice, entry 101; the final answer, entry 243 | 0.5380504 matchIn the final answer: yes (0.538)Log: n2 count_objects metrics.mean_dice, entry 41; the final answer, entry 55 |
image_patchesImage patches in the dataset.Source of the known valuePrinted in the paperMethods, field of view and patch selection, and Table 2. The paper gives 665 patches. The released files match this count in every organ folder. | 665 | exact | 665 matchNot asked in the questionLog: n1 inspect_image data.data.patterns.*/label masks/*.tif.n_files, entry 13 | 665 matchNot asked in the questionLog: n1 inspect_image data.data.patterns.*/label masks/*.tif.n_files, entry 11 | 665 matchNot asked in the questionLog: n3 run_script stdout, entry 44 | 665 matchNot asked in the questionLog: n1 inspect_image data.data.patterns.*/label masks/*.tif.n_files, entry 9 |
nuclei_label_masksNuclei in the released label masks.Source of the known valueWe calculated it with Python count of unique non-zero labels in the released label masksNot in the paper. Table 2 gives 30,698 nuclei, but the released masks hold 35,138 labels. We did not find the cause. | 35138 | exact | 35138 matchNot asked in the questionLog: n9 count_objects metrics.total_truth, entry 71 | 35138 matchNot asked in the questionLog: n12 count_objects metrics.total_truth, entry 65 | 35138 matchNot asked in the questionLog: n12 count_objects metrics.total_truth, entry 101 | 35138 matchNot asked in the questionLog: n2 count_objects metrics.total_truth, entry 41 |
Session records
Session record, Opus, run 3 of 3
Every message, decision, step and result of this run, one JSON object for each log entry.
Session record, Sonnet, run 3 of 3
Every message, decision, step and result of this run, one JSON object for each log entry.
Session record, Haiku, run 3 of 3
Every message, decision, step and result of this run, one JSON object for each log entry.
Session record, qwen3:8b
Every message, decision, step and result of this run, one JSON object for each log entry.
Opus · claude-opus-5-5 · run 3 of 3 shown 3 of 3 values match, 1 of 1 correct in the final answer
The session
This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.
setup The decision record starts with these values. A script set them before the first message, from the answers that we wrote before the run.
- Image channel for the nuclei: hematoxylinWhere the answer comes from: Not in the paper. The paper trains its models on the RGB images. Hematoxylin stains the nuclei, so we use that channel.
- Are the objects bright or dark in the channel: brightWhere the answer comes from: Not in the paper. Nuclei have high values in the hematoxylin channel after color deconvolution.
- Remove objects that touch the image edge: falseWhere the answer comes from: Not in the paper. We keep the objects at the patch edge.
- Split touching nuclei: falseWhere the answer comes from: Not in the paper for a threshold method. Our baseline does no declumping.
- Pixel size: 0Where the answer comes from: Not in the paper text. The paper names a 40x objective with numerical aperture 0.75 but gives no micrometers per pixel.
- Unit of replication: images or fieldsWhere the answer comes from: Technical Validation section. The paper splits the patches into five folds of 133 images each.
harness note A note from the harness to the model. The scientist did not write it.
- Setup values: Design: - What is the unit of replication? (replication_unit): images or fields - Pixel size in micrometers (0 = uncalibrated) (pixel_size): 0 Segmentation: - Image channel to segment (stain_channel): hematoxylin - Are the objects bright or dark in that channel? (object_polarity): bright - Remove objects that touch the image border (exclude_border_objects): false - Split touching objects with a watershed (split_touching): false Ask the scientist: Threshold method (threshold_method), Smallest object to count (pixels) (min_object_size). The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist. - Mode is now watch: you run the steps.
Model thinking, summary: I'll start by loading the skill and inspecting the data first, since I still need to clarify the threshold method and min_object_size with the user.
Model
I start with the data folder and the scoring procedure, then I ask you for the two open decisions.
The model reads the method notes
score-against-truth.The model calls
inspect_image (adapter scikit-image).step n1 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
Folder with 3325 image files in 5 pattern(s). */label masks/*.tif: 665 files, for example human bladder/label masks/human_bladder_01.tif; */mask binary/*.png: 665 files, for example human bladder/mask binary/human_bladder_01.png; */tissue images/*.png: 665 files, for example human bladder/tissue images/human_bladder_01.png; */vague areas/mask binary/*.png: 665 files, for example human bladder/vague areas/mask binary/human_bladder_01.png; */vague areas/overlay_save_path/*.png: 665 files, for example human bladder/vague areas/overlay_save_path/human_bladder_01.png
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
Tool output
{
"ok": true,
"summary": "Folder with 3325 image files in 5 pattern(s). */label masks/*.tif: 665 files, for example human bladder/label masks/human_bladder_01.tif; */mask binary/*.png: 665 files, for example human bladder/mask binary/human_bladder_01.png; */tissue images/*.png: 665 files, for example human bladder/tissue images/human_bladder_01.png; */vague areas/mask binary/*.png: 665 files, for example human bladder/vague areas/mask binary/human_bladder_01.png; */vague areas/overlay_save_path/*.png: 665 files, for example human bladder/vague areas/overlay_save_path/human_bladder_01.png",
"metrics": {
"n_files": 3325,
"n_patterns": 5
},
"data": {
"patterns": {
"*/label masks/*.tif": {
"n_files": 665,
"example": "human bladder/label masks/human_bladder_01.tif"
},
"*/mask binary/*.png": {
"n_files": 665,
"example": "human bladder/mask binary/human_bladder_01.png"
},
"*/tissue images/*.png": {
"n_files": 665,
"example": "human bladder/tissue images/human_bladder_01.png"
},
"*/vague areas/mask binary/*.png": {
"n_files": 665,
"example": "human bladder/vague areas/mask binary/human_bladder_01.png"
},
"*/vague areas/overlay_save_path/*.png": {
"n_files": 665,
"example": "human bladder/vague areas/overlay_save_path/human_bladder_01.png"
}
},
"note": "Give the folder as the path and one pattern to the segmentation tool. Give the folder of the hand-drawn masks as truth_folder and a part of its path as truth_match."
},
"outputs": []
}Model thinking, summary: The 16-bit files being label masks makes sense since those encode one number per object, so that's not an issue for intensity data. I'm curious about the 2344x1172 size appearing in 665 files — possibly the overlay_save_path — so I want to inspect one tissue image to check.
Model
The 16-bit files are the label masks, which is expected. I check one tissue image and one mask to confirm the sizes.
The model calls
inspect_image (adapter scikit-image).step n2 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
512 by 512 pixels, 3 channel(s), uint8, min 14, max 174
Input file: {data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png SHA-256 bc59dd2d355a.
Arguments
| path | {data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png |
Tool output
{
"ok": true,
"summary": "512 by 512 pixels, 3 channel(s), uint8, min 14, max 174",
"metrics": {
"width": 512,
"height": 512,
"channels": 3,
"bit_depth": 8,
"min_value": 14,
"max_value": 174,
"mean_value": 106.69461186726888,
"saturated_fraction": 0
},
"data": {
"dtype": "uint8",
"warnings": [],
"calibrated": false
},
"outputs": []
}The model calls
inspect_image (adapter scikit-image).step n3 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
512 by 512 pixels, 1 channel(s), uint16, min 0, max 64
Input file: {data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif SHA-256 9ae5584ca3c5.
Arguments
| path | {data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif |
Tool output
{
"ok": true,
"summary": "512 by 512 pixels, 1 channel(s), uint16, min 0, max 64",
"metrics": {
"width": 512,
"height": 512,
"channels": 1,
"bit_depth": 16,
"min_value": 0,
"max_value": 64,
"mean_value": 1.8787879943847656,
"saturated_fraction": 0
},
"data": {
"dtype": "uint16",
"warnings": [],
"calibrated": false
},
"outputs": []
}The model calls
inspect_image (adapter scikit-image).step n4 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
2344 by 1172 pixels, 3 channel(s), uint8, min 0, max 255
Input file: {data}/mahbod2024-nuinsseg/x/human bladder/vague areas/overlay_save_path/human_bladder_01.png SHA-256 dc15ce77bc31.
Arguments
| path | {data}/mahbod2024-nuinsseg/x/human bladder/vague areas/overlay_save_path/human_bladder_01.png |
Tool output
{
"ok": true,
"summary": "2344 by 1172 pixels, 3 channel(s), uint8, min 0, max 255",
"metrics": {
"width": 2344,
"height": 1172,
"channels": 3,
"bit_depth": 8,
"min_value": 0,
"max_value": 255,
"mean_value": 190.7022991191899,
"saturated_fraction": 0.5665454994622341
},
"data": {
"dtype": "uint8",
"warnings": [
"56.7 percent of the pixels are at the maximum value."
],
"calibrated": false
},
"outputs": []
}note Data check: tissue images are 512 x 512 pixels, RGB, 8-bit, no saturated pixels (human_bladder_01). Label masks are 16-bit TIFF, 512 x 512 pixels, one number for each nucleus. The 16-bit depth is correct for label masks and is not an intensity problem. The 2344 x 1172 files are the vague-area overlay pictures, which the analysis does not use. 665 tissue images and 665 label masks are present.
plan The model updates its plan.
- done Inspect the folder, the tissue images and the label masks
- doing Get the threshold method and the smallest object size from the scientist
- todo Outline the nuclei in all 665 tissue images (hematoxylin channel) and score each image against its label mask
- todo Compare other threshold methods as a range check
- todo Summarize Dice and F1 per organ and overall, with a figure and tables
Model thinking, summary: I still need the threshold method and minimum object size before running the count—calling count_objects now would likely block. I should propose these values to the scientist instead of guessing.
Model
Two decisions are open: the threshold method and the smallest object size. I propose values for both.
The model calls
propose_change.Result of propose_change: The record already says "otsu" for Threshold method.
The model calls
propose_change.decision card Change Smallest object to count (pixels)?
The model asks to change "Smallest object to count (pixels)" from 0 to 30. Reason: At 512 x 512 pixels, a nucleus in these patches is usually much larger than 30 pixels. A 30-pixel filter removes noise specks of hematoxylin but keeps small nuclei. The pixel size is not given, so the value is in pixels.
Options: Change to 30 Keep 0
Suggested: 30 (At 512 x 512 pixels, a nucleus in these patches is usually much larger than 30 pixels. A 30-pixel filter removes noise specks of hematoxylin but keeps small nuclei. The pixel size is not given, so the value is in pixels.)
Answer 30
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper states no size range. We remove objects under 30 pixels.
Result of propose_change: The scientist answered. The record now says 30 for Smallest object to count (pixels).
Model
The decisions are set: Otsu threshold and a 30-pixel smallest object. Next, I outline the nuclei in all 665 tissue images and score each one against its label mask.
The model calls
count_objects (adapter scikit-image).paused The harness paused count_objects until the scientist chose: Threshold method. The decision cards follow.
Comparing the options for "Threshold method" before it asks the scientist.
comparison run n5 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-1/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-1/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (412 more characters in the session record)comparison run n6 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 65132 objects; mean Dice 0.512 over 665 scored images
Outputs: per_group.csv (0795c84577e0), per_image.csv (88ac22ea3040).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | li |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 65132 objects; mean Dice 0.512 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 65132,
"total_objects": 65132,
"mean_objects_per_image": 97.94285714285714,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5118254135338345,
"mean_f1_iou_50": 0.1059387969924812,
"mean_average_f1": 0.04181624060150376
},
"outputs": [
{
"path": "{work}/count_objects-2/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-2/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
787,
65.58,
453,
0.5916
],
[
"human brain",
12,
746,
62.17,
150,
0.1103
],
[
"human cardia",
12,
547,
45.58,
767,
0.6625
],
[
"human cerebellum",
12,
948,
79,
653,
0.6205
],
[
"human epiglottis",
11,
669,
60.82,
261,
0.4571
],
[
"human jejunum",
10,
714,
71.4,
1033,
0.7355
],
[
"human kidney",
11,
442,
40.18,
1521,
0.8229
],
[
"human liver",
40,
7014,
175.35,
1471,
0.5512
],
[
"human lung",
11,
602,
54.73,
364,
0.5617
],
[
"human melanoma",
12,
710,
59.17,
584,
0.4275
],
[
"human muscle",
9,
1148,
127.56,
137,
0.2211
],
[
"human oesophagus",
47,
5814,
123.7,
2329,
0.6461
],
[
"human pancreas",
44,
2089,
47.48,
2436,
0.7209
],
[
"human peritoneum",
12,
533,
44.42,
511,
0.4927
],
[
"human placenta",
40,
2323,
58.08,
2415,
0.7878
],
[
"human pylorus",
12,
727,
60.58,
481,
0.6296
],
[
"human rectum",
12,
1065,
88.75,
390,
0.4665
],
[
"human salivory gland",
44,
3586,
81.5,
3577,
0.7177
],
[
"human spleen",
34,
2335,
68.68,
3876,
0.6939
],
[
"human testis",
12,
1341,
111.75,
406,
0.4466
],
[
"human tongue",
40,
2151,
53.77,
1628,
0.6604
],
[
"human tonsile",
12,
653,
54.42,
1166,
0.5766
],
[
"human umbilical cord",
11,
1224,
111.27,
117,
0.0719
],
[
"mouse fat (white and brown)_subscapula",
42,
3796,
90.38,
557,
0.0908
],
[
"mouse femur",
6,
731,
121.83,
921,
0.6908
],
[
"mouse heart",
28,
5149,
183.89,
743,
0.1688
],
[
"mouse kidney",
40,
6074,
151.85,
1625,
0.
... (409 more characters in the session record)comparison run n7 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66621 objects; mean Dice 0.531 over 665 scored images
Outputs: per_group.csv (24995c7336ff), per_image.csv (9b74743a65a9).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | isodata |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66621 objects; mean Dice 0.531 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66621,
"total_objects": 66621,
"mean_objects_per_image": 100.18195488721804,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5313412030075189,
"mean_f1_iou_50": 0.13456496240601504,
"mean_average_f1": 0.05140496240601504
},
"outputs": [
{
"path": "{work}/count_objects-3/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-3/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
399,
33.25,
453,
0.7038
],
[
"human brain",
12,
1056,
88,
150,
0.1256
],
[
"human cardia",
12,
476,
39.67,
767,
0.7199
],
[
"human cerebellum",
12,
981,
81.75,
653,
0.4136
],
[
"human epiglottis",
11,
560,
50.91,
261,
0.554
],
[
"human jejunum",
10,
673,
67.3,
1033,
0.7298
],
[
"human kidney",
11,
537,
48.82,
1521,
0.7872
],
[
"human liver",
40,
6216,
155.4,
1471,
0.5846
],
[
"human lung",
11,
375,
34.09,
364,
0.5232
],
[
"human melanoma",
12,
666,
55.5,
584,
0.4862
],
[
"human muscle",
9,
1218,
135.33,
137,
0.2516
],
[
"human oesophagus",
47,
6244,
132.85,
2329,
0.6615
],
[
"human pancreas",
44,
2196,
49.91,
2436,
0.7428
],
[
"human peritoneum",
12,
527,
43.92,
511,
0.546
],
[
"human placenta",
40,
2312,
57.8,
2415,
0.7807
],
[
"human pylorus",
12,
451,
37.58,
481,
0.717
],
[
"human rectum",
12,
921,
76.75,
390,
0.5529
],
[
"human salivory gland",
44,
3476,
79,
3577,
0.7189
],
[
"human spleen",
34,
2406,
70.76,
3876,
0.6822
],
[
"human testis",
12,
1303,
108.58,
406,
0.514
],
[
"human tongue",
40,
1592,
39.8,
1628,
0.7219
],
[
"human tonsile",
12,
852,
71,
1166,
0.6027
],
[
"human umbilical cord",
11,
1122,
102,
117,
0.0839
],
[
"mouse fat (white and brown)_subscapula",
42,
4892,
116.48,
557,
0.0964
],
[
"mouse femur",
6,
683,
113.83,
921,
0.6718
],
[
"mouse heart",
28,
5723,
204.39,
743,
0.1887
],
[
"mouse kidney",
40,
6316,
157.9,
1625,
0.4242
],
... (395 more characters in the session record)comparison run n8 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 16395 objects; mean Dice 0.355 over 665 scored images
Outputs: per_group.csv (e2995278e1af), per_image.csv (9d162c48af46).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | triangle |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 16395 objects; mean Dice 0.355 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 16395,
"total_objects": 16395,
"mean_objects_per_image": 24.654135338345863,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.3551672180451128,
"mean_f1_iou_50": 0.13387774436090225,
"mean_average_f1": 0.04810541353383459
},
"outputs": [
{
"path": "{work}/count_objects-4/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-4/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
252,
21,
453,
0.6816
],
[
"human brain",
12,
131,
10.92,
150,
0.5475
],
[
"human cardia",
12,
292,
24.33,
767,
0.5184
],
[
"human cerebellum",
12,
256,
21.33,
653,
0.73
],
[
"human epiglottis",
11,
144,
13.09,
261,
0.7523
],
[
"human jejunum",
10,
234,
23.4,
1033,
0.0939
],
[
"human kidney",
11,
392,
35.64,
1521,
0.1675
],
[
"human liver",
40,
1103,
27.57,
1471,
0.1507
],
[
"human lung",
11,
203,
18.45,
364,
0.6712
],
[
"human melanoma",
12,
345,
28.75,
584,
0.4204
],
[
"human muscle",
9,
168,
18.67,
137,
0.5278
],
[
"human oesophagus",
47,
1103,
23.47,
2329,
0.2734
],
[
"human pancreas",
44,
1314,
29.86,
2436,
0.3661
],
[
"human peritoneum",
12,
244,
20.33,
511,
0.2299
],
[
"human placenta",
40,
1255,
31.38,
2415,
0.6112
],
[
"human pylorus",
12,
364,
30.33,
481,
0.6706
],
[
"human rectum",
12,
283,
23.58,
390,
0.5956
],
[
"human salivory gland",
44,
2067,
46.98,
3577,
0.2667
],
[
"human spleen",
34,
978,
28.76,
3876,
0.1726
],
[
"human testis",
12,
347,
28.92,
406,
0.4555
],
[
"human tongue",
40,
892,
22.3,
1628,
0.5831
],
[
"human tonsile",
12,
212,
17.67,
1166,
0.0661
],
[
"human umbilical cord",
11,
156,
14.18,
117,
0.5915
],
[
"mouse fat (white and brown)_subscapula",
42,
455,
10.83,
557,
0.2438
],
[
"mouse femur",
6,
271,
45.17,
921,
0.2172
],
[
"mouse heart",
28,
519,
18.54,
743,
0.291
],
[
"mouse kidney",
40,
918,
22.95,
1625,
0.1546
],
[
... (383 more characters in the session record)comparison Comparison runs for Threshold method. The record keeps the scientist's choice.
Threshold method n_objects Result otsu 66610 ok li 65132 ok isodata 66621 ok triangle 16395 ok
decision card Threshold method
The threshold decides which pixels are objects. Different methods can give different counts. The model runs the main methods and shows you the counts before you choose. The model wants to run count_objects.
Options: otsu li triangle isodata yen mean minimum
Suggested: otsu (The model proposed this value when it asked to run the step.)
Data that the model gave for this card
Threshold method n_objects Result otsu 66610 ok li 65132 ok isodata 66621 ok triangle 16395 ok n_objects depends on the choice: 66610 with otsu, 65132 with li, 66621 with isodata, 16395 with triangle
Answer otsu
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper uses deep learning. We use a global Otsu threshold as a simple baseline.
step n9 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-5/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-5/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (412 more characters in the session record)Model thinking, summary: I'm seeing 66610 detected versus a ground truth of 35138, almost double — pointing to over-segmentation into fragments. I should check the per-image breakdown and compare threshold method options to narrow down the cause.
Model
The run found 66610 objects against 35138 hand-drawn nuclei. That suggests many small fragments. Next, I read the per-image and per-organ tables to see where the scores differ.
The model runs run_script.
import pandas as pd
d='count_objects-5/'
pi=pd.read_csv(d+'per_image.csv'); pg=pd.read_csv(d+'per_group.csv')
print(pi.columns.tolist()); print(pi.head(3).to_string())
print(pg.columns.tolist())
num=[c for c in pi.columns if pi[c].dtype!=object]
g=pi.groupby('group')[num].mean(numeric_only=True)
cols=[c for c in ['objects','n_objects','truth','n_truth','dice','f1_iou_50','f1_iou_70','average_f1','missed_fraction'] if c in pi.columns]
print(pi[cols].describe().to_string())
print(pi.groupby('group')[cols].mean().round(3).to_string())
The harness lets the model use
run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.step n10 run_script
The script ran in {work} and wrote 0 new file(s) to {work}.
Arguments
| purpose | Summarize the scores per organ and look at the found-to-truth count ratio. |
| does | Opens the per-image and per-organ score tables, shows the columns, and lists per organ the image count, found count, truth count, mean Dice and mean F1. |
| code | import pandas as pd
d='count_objects-5/'
pi=pd.read_csv(d+'per_image.csv'); pg=pd.read_csv(d+'per_group.csv')
print(pi.columns.tolist()); print(pi.head(3).to_string())
print(pg.columns.tolist())
num=[c for c in pi.columns if pi[c].dtype!=object]
g=pi.groupby('group')[num].mean(numeric_only=True)
cols=[c for c in ['objects','n_objects','truth','n_truth','dice','f1_iou_50','f1_iou_70','average_f1',' ... |
Tool output
['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
image group n_objects mean_area_px n_truth dice f1_iou_50 average_f1
0 human bladder/tissue images/human_bladder_01.png human bladder 42 1351.10 29 0.6763 0.0845 0.0310
1 human bladder/tissue images/human_bladder_02.png human bladder 49 396.73 3 0.5021 0.0769 0.0308
2 human bladder/tissue images/human_bladder_03.png human bladder 26 1141.62 30 0.7831 0.2143 0.0929
['group', 'n_images', 'total_objects', 'objects_per_image', 'total_truth', 'mean_dice']
n_objects n_truth dice f1_iou_50 average_f1
count 665.000000 665.000000 665.000000 665.000000 665.000000
mean 100.165414 52.839098 0.538050 0.148013 0.057802
std 75.827203 54.041898 0.265251 0.161680 0.077059
min 1.000000 1.000000 0.000000 0.000000 0.000000
25% 41.000000 20.000000 0.330300 0.022400 0.004900
50% 72.000000 38.000000 0.643400 0.100000 0.031100
75% 153.000000 66.000000 0.760900 0.229200 0.085300
max 407.000000 370.000000 0.910400 1.000000 0.700000
n_objects n_truth dice f1_iou_50 average_f1
group
human bladder 29.417 37.750 0.732 0.289 0.100
human brain 97.083 12.500 0.132 0.017 0.005
human cardia 38.667 63.917 0.728 0.271 0.109
human cerebellum 33.250 54.417 0.593 0.418 0.247
human epiglottis 32.091 23.727 0.646 0.218 0.116
human jejunum 68.300 103.300 0.727 0.094 0.030
human kidney 50.818 138.273 0.779 0.151 0.051
human liver 146.875 36.775 0.593 0.100 0.030
human lung 27.364 33.091 0.634 0.278 0.138
human melanoma 54.083 48.667 0.507 0.171 0.062
human muscle 136.444 15.222 0.259 0.035 0.011
human oesophagus 132.511 49.553 0.667 0.144 0.054
human pancreas 50.727 55.364 0.746 0.318 0.136
human peritoneum 41.250 42.583 0.558 0.102 0.038
human placenta 56.875 60.375 0.780 0.180 0.077
human pylorus 35.583 40.083 0.740 0.399 0.154
human rectum 68.250 32.500 0.589 0.138 0.048
human
... (1000 more characters in the session record)Model thinking, summary: In dense organs, nuclei merge and get undercounted; in sparse organs like brain, muscle, cord, and fat, overcounting drives Dice scores down. I'll now run the other threshold methods as a range check without altering the record.
The model calls
compare_options.comparison run n11 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 65132 objects; mean Dice 0.512 over 665 scored images
Outputs: per_group.csv (0795c84577e0), per_image.csv (88ac22ea3040).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | li |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 65132 objects; mean Dice 0.512 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 65132,
"total_objects": 65132,
"mean_objects_per_image": 97.94285714285714,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5118254135338345,
"mean_f1_iou_50": 0.1059387969924812,
"mean_average_f1": 0.04181624060150376
},
"outputs": [
{
"path": "{work}/count_objects-6/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-6/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
787,
65.58,
453,
0.5916
],
[
"human brain",
12,
746,
62.17,
150,
0.1103
],
[
"human cardia",
12,
547,
45.58,
767,
0.6625
],
[
"human cerebellum",
12,
948,
79,
653,
0.6205
],
[
"human epiglottis",
11,
669,
60.82,
261,
0.4571
],
[
"human jejunum",
10,
714,
71.4,
1033,
0.7355
],
[
"human kidney",
11,
442,
40.18,
1521,
0.8229
],
[
"human liver",
40,
7014,
175.35,
1471,
0.5512
],
[
"human lung",
11,
602,
54.73,
364,
0.5617
],
[
"human melanoma",
12,
710,
59.17,
584,
0.4275
],
[
"human muscle",
9,
1148,
127.56,
137,
0.2211
],
[
"human oesophagus",
47,
5814,
123.7,
2329,
0.6461
],
[
"human pancreas",
44,
2089,
47.48,
2436,
0.7209
],
[
"human peritoneum",
12,
533,
44.42,
511,
0.4927
],
[
"human placenta",
40,
2323,
58.08,
2415,
0.7878
],
[
"human pylorus",
12,
727,
60.58,
481,
0.6296
],
[
"human rectum",
12,
1065,
88.75,
390,
0.4665
],
[
"human salivory gland",
44,
3586,
81.5,
3577,
0.7177
],
[
"human spleen",
34,
2335,
68.68,
3876,
0.6939
],
[
"human testis",
12,
1341,
111.75,
406,
0.4466
],
[
"human tongue",
40,
2151,
53.77,
1628,
0.6604
],
[
"human tonsile",
12,
653,
54.42,
1166,
0.5766
],
[
"human umbilical cord",
11,
1224,
111.27,
117,
0.0719
],
[
"mouse fat (white and brown)_subscapula",
42,
3796,
90.38,
557,
0.0908
],
[
"mouse femur",
6,
731,
121.83,
921,
0.6908
],
[
"mouse heart",
28,
5149,
183.89,
743,
0.1688
],
[
"mouse kidney",
40,
6074,
151.85,
1625,
0.
... (409 more characters in the session record)comparison run n12 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 16395 objects; mean Dice 0.355 over 665 scored images
Outputs: per_group.csv (e2995278e1af), per_image.csv (9d162c48af46).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | triangle |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 16395 objects; mean Dice 0.355 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 16395,
"total_objects": 16395,
"mean_objects_per_image": 24.654135338345863,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.3551672180451128,
"mean_f1_iou_50": 0.13387774436090225,
"mean_average_f1": 0.04810541353383459
},
"outputs": [
{
"path": "{work}/count_objects-7/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-7/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
252,
21,
453,
0.6816
],
[
"human brain",
12,
131,
10.92,
150,
0.5475
],
[
"human cardia",
12,
292,
24.33,
767,
0.5184
],
[
"human cerebellum",
12,
256,
21.33,
653,
0.73
],
[
"human epiglottis",
11,
144,
13.09,
261,
0.7523
],
[
"human jejunum",
10,
234,
23.4,
1033,
0.0939
],
[
"human kidney",
11,
392,
35.64,
1521,
0.1675
],
[
"human liver",
40,
1103,
27.57,
1471,
0.1507
],
[
"human lung",
11,
203,
18.45,
364,
0.6712
],
[
"human melanoma",
12,
345,
28.75,
584,
0.4204
],
[
"human muscle",
9,
168,
18.67,
137,
0.5278
],
[
"human oesophagus",
47,
1103,
23.47,
2329,
0.2734
],
[
"human pancreas",
44,
1314,
29.86,
2436,
0.3661
],
[
"human peritoneum",
12,
244,
20.33,
511,
0.2299
],
[
"human placenta",
40,
1255,
31.38,
2415,
0.6112
],
[
"human pylorus",
12,
364,
30.33,
481,
0.6706
],
[
"human rectum",
12,
283,
23.58,
390,
0.5956
],
[
"human salivory gland",
44,
2067,
46.98,
3577,
0.2667
],
[
"human spleen",
34,
978,
28.76,
3876,
0.1726
],
[
"human testis",
12,
347,
28.92,
406,
0.4555
],
[
"human tongue",
40,
892,
22.3,
1628,
0.5831
],
[
"human tonsile",
12,
212,
17.67,
1166,
0.0661
],
[
"human umbilical cord",
11,
156,
14.18,
117,
0.5915
],
[
"mouse fat (white and brown)_subscapula",
42,
455,
10.83,
557,
0.2438
],
[
"mouse femur",
6,
271,
45.17,
921,
0.2172
],
[
"mouse heart",
28,
519,
18.54,
743,
0.291
],
[
"mouse kidney",
40,
918,
22.95,
1625,
0.1546
],
[
... (383 more characters in the session record)comparison run n13 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66621 objects; mean Dice 0.531 over 665 scored images
Outputs: per_group.csv (24995c7336ff), per_image.csv (9b74743a65a9).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | isodata |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66621 objects; mean Dice 0.531 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66621,
"total_objects": 66621,
"mean_objects_per_image": 100.18195488721804,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5313412030075189,
"mean_f1_iou_50": 0.13456496240601504,
"mean_average_f1": 0.05140496240601504
},
"outputs": [
{
"path": "{work}/count_objects-8/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-8/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
399,
33.25,
453,
0.7038
],
[
"human brain",
12,
1056,
88,
150,
0.1256
],
[
"human cardia",
12,
476,
39.67,
767,
0.7199
],
[
"human cerebellum",
12,
981,
81.75,
653,
0.4136
],
[
"human epiglottis",
11,
560,
50.91,
261,
0.554
],
[
"human jejunum",
10,
673,
67.3,
1033,
0.7298
],
[
"human kidney",
11,
537,
48.82,
1521,
0.7872
],
[
"human liver",
40,
6216,
155.4,
1471,
0.5846
],
[
"human lung",
11,
375,
34.09,
364,
0.5232
],
[
"human melanoma",
12,
666,
55.5,
584,
0.4862
],
[
"human muscle",
9,
1218,
135.33,
137,
0.2516
],
[
"human oesophagus",
47,
6244,
132.85,
2329,
0.6615
],
[
"human pancreas",
44,
2196,
49.91,
2436,
0.7428
],
[
"human peritoneum",
12,
527,
43.92,
511,
0.546
],
[
"human placenta",
40,
2312,
57.8,
2415,
0.7807
],
[
"human pylorus",
12,
451,
37.58,
481,
0.717
],
[
"human rectum",
12,
921,
76.75,
390,
0.5529
],
[
"human salivory gland",
44,
3476,
79,
3577,
0.7189
],
[
"human spleen",
34,
2406,
70.76,
3876,
0.6822
],
[
"human testis",
12,
1303,
108.58,
406,
0.514
],
[
"human tongue",
40,
1592,
39.8,
1628,
0.7219
],
[
"human tonsile",
12,
852,
71,
1166,
0.6027
],
[
"human umbilical cord",
11,
1122,
102,
117,
0.0839
],
[
"mouse fat (white and brown)_subscapula",
42,
4892,
116.48,
557,
0.0964
],
[
"mouse femur",
6,
683,
113.83,
921,
0.6718
],
[
"mouse heart",
28,
5723,
204.39,
743,
0.1887
],
[
"mouse kidney",
40,
6316,
157.9,
1625,
0.4242
],
... (395 more characters in the session record)comparison run n14 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 16574 objects; mean Dice 0.305 over 665 scored images
Outputs: per_group.csv (0e55aec7f97d), per_image.csv (f56d00615975).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | yen |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 16574 objects; mean Dice 0.305 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 16574,
"total_objects": 16574,
"mean_objects_per_image": 24.923308270676692,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.30511323308270677,
"mean_f1_iou_50": 0.10875067669172933,
"mean_average_f1": 0.03850315789473684
},
"outputs": [
{
"path": "{work}/count_objects-9/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-9/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
202,
16.83,
453,
0.6347
],
[
"human brain",
12,
86,
7.17,
150,
0.4074
],
[
"human cardia",
12,
292,
24.33,
767,
0.5025
],
[
"human cerebellum",
12,
132,
11,
653,
0.3815
],
[
"human epiglottis",
11,
26,
2.36,
261,
0.2332
],
[
"human jejunum",
10,
487,
48.7,
1033,
0.3297
],
[
"human kidney",
11,
473,
43,
1521,
0.1903
],
[
"human liver",
40,
1032,
25.8,
1471,
0.1363
],
[
"human lung",
11,
121,
11,
364,
0.3486
],
[
"human melanoma",
12,
210,
17.5,
584,
0.2698
],
[
"human muscle",
9,
126,
14,
137,
0.4549
],
[
"human oesophagus",
47,
1433,
30.49,
2329,
0.3611
],
[
"human pancreas",
44,
1010,
22.95,
2436,
0.3309
],
[
"human peritoneum",
12,
333,
27.75,
511,
0.3832
],
[
"human placenta",
40,
1168,
29.2,
2415,
0.5556
],
[
"human pylorus",
12,
256,
21.33,
481,
0.5209
],
[
"human rectum",
12,
312,
26,
390,
0.6258
],
[
"human salivory gland",
44,
2215,
50.34,
3577,
0.3344
],
[
"human spleen",
34,
1163,
34.21,
3876,
0.1875
],
[
"human testis",
12,
498,
41.5,
406,
0.5581
],
[
"human tongue",
40,
841,
21.02,
1628,
0.5379
],
[
"human tonsile",
12,
580,
48.33,
1166,
0.2231
],
[
"human umbilical cord",
11,
68,
6.18,
117,
0.439
],
[
"mouse fat (white and brown)_subscapula",
42,
769,
18.31,
557,
0.0836
],
[
"mouse femur",
6,
361,
60.17,
921,
0.2441
],
[
"mouse heart",
28,
296,
10.57,
743,
0.1627
],
[
"mouse kidney",
40,
479,
11.97,
1625,
0.076
],
[
"mouse liver",
... (370 more characters in the session record)comparison run n15 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 76603 objects; mean Dice 0.470 over 665 scored images
Outputs: per_group.csv (e5fe5de0859a), per_image.csv (d2658cb39713).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | mean |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 76603 objects; mean Dice 0.470 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 76603,
"total_objects": 76603,
"mean_objects_per_image": 115.19248120300752,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.4699881203007519,
"mean_f1_iou_50": 0.06568075187969925,
"mean_average_f1": 0.02456992481203007
},
"outputs": [
{
"path": "{work}/count_objects-10/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-10/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
1741,
145.08,
453,
0.4363
],
[
"human brain",
12,
924,
77,
150,
0.1157
],
[
"human cardia",
12,
1134,
94.5,
767,
0.5235
],
[
"human cerebellum",
12,
1276,
106.33,
653,
0.4214
],
[
"human epiglottis",
11,
1151,
104.64,
261,
0.2233
],
[
"human jejunum",
10,
774,
77.4,
1033,
0.7304
],
[
"human kidney",
11,
434,
39.45,
1521,
0.829
],
[
"human liver",
40,
7355,
183.88,
1471,
0.5426
],
[
"human lung",
11,
859,
78.09,
364,
0.3811
],
[
"human melanoma",
12,
772,
64.33,
584,
0.3697
],
[
"human muscle",
9,
1261,
140.11,
137,
0.2059
],
[
"human oesophagus",
47,
6699,
142.53,
2329,
0.6214
],
[
"human pancreas",
44,
2152,
48.91,
2436,
0.6586
],
[
"human peritoneum",
12,
598,
49.83,
511,
0.4884
],
[
"human placenta",
40,
2919,
72.97,
2415,
0.7538
],
[
"human pylorus",
12,
1653,
137.75,
481,
0.4597
],
[
"human rectum",
12,
1145,
95.42,
390,
0.4104
],
[
"human salivory gland",
44,
3916,
89,
3577,
0.695
],
[
"human spleen",
34,
2807,
82.56,
3876,
0.6448
],
[
"human testis",
12,
1484,
123.67,
406,
0.4084
],
[
"human tongue",
40,
3489,
87.22,
1628,
0.5292
],
[
"human tonsile",
12,
791,
65.92,
1166,
0.5903
],
[
"human umbilical cord",
11,
1218,
110.73,
117,
0.0676
],
[
"mouse fat (white and brown)_subscapula",
42,
5450,
129.76,
557,
0.0958
],
[
"mouse femur",
6,
919,
153.17,
921,
0.685
],
[
"mouse heart",
28,
5381,
192.18,
743,
0.1732
],
[
"mouse kidney",
40,
6262,
156.55,
162
... (419 more characters in the session record)comparison Comparison runs for Threshold method. The record keeps the scientist's choice.
Threshold method n_objects Result li 65132 ok triangle 16395 ok isodata 66621 ok yen 16574 ok mean 76603 ok
Result of compare_options: Compared 5 values of Threshold method as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, trut ...
Compared 5 values of Threshold method as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, truth_folder={data}/mahbod2024-nuinsseg/x, truth_match=label masks, stain_channel=hematoxylin, object_polarity=bright, min_object_size=30, exclude_border_objects=false, split_touching=false, pixel_size=0). Only that step was repeated. To compare another step, run it again first. The record still says "otsu".
Threshold method n_objects Result
li 65132 ok
triangle 16395 ok
isodata 66621 ok
yen 16574 ok
mean 76603 ok
n_objects depends on the choice: 65132 with li, 16395 with triangle, 66621 with isodata, 16574 with yen, 76603 with meanModel thinking, summary: Only object counts came back, not Dice/F1 scores, so I'm missing the per-method comparison data I actually need. I should check if there are output folders with those results rather than just rerunning count_objects, since switching methods now would count as a logged deviation.
Model
The comparison shows only the counts. The "minimum" method has no result in the table, so I do not report it. Next, I look for the score tables of the comparison runs.
The model calls
list_dir.Result of list_dir: 32 entries in {work}.
- ._count_objects-1 4096 - ._count_objects-10 4096 - ._count_objects-2 4096 - ._count_objects-3 4096 - ._count_objects-4 4096 - ._count_objects-5 4096 - ._count_objects-6 4096 - ._count_objects-7 4096 - ._count_objects-8 4096 - ._count_objects-9 4096 - ._inspect_image-1 4096 - ._inspect_image-2 4096 - ._inspect_image-3 4096 - ._inspect_image-4 4096 - ._samples.csv 4096 - ._script-1 4096 d count_objects-1 d count_objects-10 d count_objects-2 d count_objects-3 d count_objects-4 d count_objects-5 d count_objects-6 d count_objects-7 d count_objects-8 d count_objects-9 d inspect_image-1 d inspect_image-2 d inspect_image-3 d inspect_image-4 - samples.csv 473879 d script-1
The model runs run_script.
import pandas as pd, os, glob
known={66610:'otsu',65132:'li',16395:'triangle',66621:'isodata',16574:'yen',76603:'mean'}
rows=[]
for d in sorted(glob.glob('count_objects-*')):
f=os.path.join(d,'per_image.csv')
if not os.path.exists(f): print(d,'no table'); continue
p=pd.read_csv(f)
n=int(p.n_objects.sum())
print(d,len(p),n,known.get(n))
if n in known and len(p)==665:
rows.append(dict(method=known[n],run=d,n_images=len(p),found_objects=n,truth_objects=int(p.n_truth.sum()),
mean_dice=round(p.dice.mean(),4),median_dice=round(p.dice.median(),4),mean_f1_iou_50=round(p.f1_iou_50.mean(),4),mean_average_f1=round(p.average_f1.mean(),4)))
df=pd.DataFrame(rows).drop_duplicates('method')
print(df.to_string())
from ga_api import save_table
save_table('method_comparison',df.to_dict('records'))
The harness lets the model use
run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.step n16 run_script
The script ran in {work} and wrote 1 new file(s) to {work}.
Outputs: method_comparison.csv (2be9fa7f6b27).
Arguments
| purpose | Collect Dice and F1 for each threshold method from the comparison runs. |
| does | Opens each per-image score table, matches it to its threshold method by the found count, and writes one table with the found count, truth count, mean Dice and mean F1 per method (method_comparison.csv). |
| code | import pandas as pd, os, glob
known={66610:'otsu',65132:'li',16395:'triangle',66621:'isodata',16574:'yen',76603:'mean'}
rows=[]
for d in sorted(glob.glob('count_objects-*')):
f=os.path.join(d,'per_image.csv')
if not os.path.exists(f): print(d,'no table'); continue
p=pd.read_csv(f)
n=int(p.n_objects.sum())
print(d,len(p),n,known.get(n))
if n in known and len(p)==665: ... |
Tool output
count_objects-1 665 66610 otsu
count_objects-10 665 76603 mean
count_objects-2 665 65132 li
count_objects-3 665 66621 isodata
count_objects-4 665 16395 triangle
count_objects-5 665 66610 otsu
count_objects-6 665 65132 li
count_objects-7 665 16395 triangle
count_objects-8 665 66621 isodata
count_objects-9 665 16574 yen
method run n_images found_objects truth_objects mean_dice median_dice mean_f1_iou_50 mean_average_f1
0 otsu count_objects-1 665 66610 35138 0.5381 0.6434 0.1480 0.0578
1 mean count_objects-10 665 76603 35138 0.4700 0.4916 0.0657 0.0246
2 li count_objects-2 665 65132 35138 0.5118 0.5729 0.1059 0.0418
3 isodata count_objects-3 665 66621 35138 0.5313 0.6347 0.1346 0.0514
4 triangle count_objects-4 665 16395 35138 0.3552 0.2793 0.1339 0.0481
9 yen count_objects-9 665 16574 35138 0.3051 0.2115 0.1088 0.0385
Model
Otsu gives the highest mean Dice of the methods. All methods give a low F1. Next, I check if a watershed split of touching nuclei changes the object match, as a comparison run only.
The model calls
compare_options.comparison run n17 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 295536 objects; mean Dice 0.538 over 665 scored images
Outputs: per_group.csv (cf3147293035), per_image.csv (e3bb62b8d47d).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | true |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 295536 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 295536,
"total_objects": 295536,
"mean_objects_per_image": 444.41503759398495,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5384652631578948,
"mean_f1_iou_50": 0.09383458646616542,
"mean_average_f1": 0.03374962406015038
},
"outputs": [
{
"path": "{work}/count_objects-11/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-11/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
1047,
87.25,
453,
0.7328
],
[
"human brain",
12,
8618,
718.17,
150,
0.1329
],
[
"human cardia",
12,
1558,
129.83,
767,
0.728
],
[
"human cerebellum",
12,
834,
69.5,
653,
0.5931
],
[
"human epiglottis",
11,
1384,
125.82,
261,
0.6456
],
[
"human jejunum",
10,
4075,
407.5,
1033,
0.7266
],
[
"human kidney",
11,
3127,
284.27,
1521,
0.7789
],
[
"human liver",
40,
23265,
581.62,
1471,
0.5937
],
[
"human lung",
11,
990,
90,
364,
0.6337
],
[
"human melanoma",
12,
2207,
183.92,
584,
0.5078
],
[
"human muscle",
9,
5244,
582.67,
137,
0.2594
],
[
"human oesophagus",
47,
23813,
506.66,
2329,
0.6674
],
[
"human pancreas",
44,
6833,
155.3,
2436,
0.7457
],
[
"human peritoneum",
12,
3330,
277.5,
511,
0.5587
],
[
"human placenta",
40,
9486,
237.15,
2415,
0.7803
],
[
"human pylorus",
12,
1415,
117.92,
481,
0.7405
],
[
"human rectum",
12,
3010,
250.83,
390,
0.5899
],
[
"human salivory gland",
44,
12830,
291.59,
3577,
0.7175
],
[
"human spleen",
34,
6013,
176.85,
3876,
0.5405
],
[
"human testis",
12,
4382,
365.17,
406,
0.5282
],
[
"human tongue",
40,
5376,
134.4,
1628,
0.7429
],
[
"human tonsile",
12,
5186,
432.17,
1166,
0.6078
],
[
"human umbilical cord",
11,
6819,
619.91,
117,
0.0921
],
[
"mouse fat (white and brown)_subscapula",
42,
51177,
1218.5,
557,
0.1033
],
[
"mouse femur",
6,
2096,
349.33,
921,
0.6642
],
[
"mouse heart",
28,
24038,
858.5,
743,
0.2029
],
[
"mouse kidney",
40,
... (452 more characters in the session record)comparison Comparison runs for Split touching nuclei. The record keeps the scientist's choice.
Split touching objects with a watershed n_images n_objects total_objects mean_objects_per_image n_groups n_scored total_truth mean_dice mean_f1_iou_50 mean_average_f1 Result true 665 295536 295536 444.4 31 665 35138 0.5385 0.09383 0.03375 ok
Result of compare_options: Compared 1 values of Split touching objects with a watershed as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/ti ...
Compared 1 values of Split touching objects with a watershed as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, truth_folder={data}/mahbod2024-nuinsseg/x, truth_match=label masks, stain_channel=hematoxylin, object_polarity=bright, threshold_method=otsu, min_object_size=30, exclude_border_objects=false, pixel_size=0). Only that step was repeated. To compare another step, run it again first. The record still says false.
Split touching objects with a watershed n_images n_objects total_objects mean_objects_per_image n_groups n_scored total_truth mean_dice mean_f1_iou_50 mean_average_f1 Result
true 665 295536 295536 444.4 31 665 35138 0.5385 0.09383 0.03375 okModel thinking, summary: The watershed approach actually performed worse, with an F1 of just 0.094. I should put together a per-organ breakdown showing Dice and F1 scores for Otsu, along with a table, and note the found-versus-truth ratio of 66610 to 35138.
Model
The watershed split makes the match worse: F1 drops and the count rises to 295536. Next, I make a per-organ table and a figure for the Otsu run.
The model runs run_script.
import pandas as pd, matplotlib; matplotlib.use('Agg'); import matplotlib.pyplot as plt
from ga_api import save_table, save_figure
p=pd.read_csv('count_objects-5/per_image.csv')
g=p.groupby('group').agg(n_images=('image','count'),found_objects=('n_objects','sum'),truth_objects=('n_truth','sum'),
mean_dice=('dice','mean'),sd_dice=('dice','std'),mean_f1_iou_50=('f1_iou_50','mean'),sd_f1_iou_50=('f1_iou_50','std'),mean_average_f1=('average_f1','mean')).reset_index()
g['found_to_truth_ratio']=g.found_objects/g.truth_objects
g=g.round(3).sort_values('mean_dice')
save_table('per_organ_scores',g.to_dict('records'))
print(g.to_string(index=False))
fig,ax=plt.subplots(figsize=(8,9))
y=range(len(g))
ax.barh([i+0.2 for i in y],g.mean_dice,height=0.4,xerr=g.sd_dice,label='Dice (pixel overlap)',color='tab:blue')
ax.barh([i-0.2 for i in y],g.mean_f1_iou_50,height=0.4,xerr=g.sd_f1_iou_50,label='F1 at IoU 0.5 (object match)',color='tab:orange')
ax.set_yticks(list(y)); ax.set_yticklabels([f"{a} (n={b})" for a,b in zip(g.group,g.n_images)],fontsize=7)
ax.set_xlabel('Score per image, mean ± SD (unitless, 0 to 1)'); ax.set_xlim(0,1)
ax.set_title('Otsu on hematoxylin, min 30 px, no watershed\nn = images per organ')
ax.legend(loc='lower right',fontsize=8); plt.tight_layout()
save_figure('per_organ_dice_f1.png')
The harness lets the model use
run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.step n18 run_script
The script ran in {work} and wrote 3 new file(s) to {work}.
Outputs: per_organ_dice_f1.png (c73d2056b56b), per_organ_dice_f1.svg (6ebb05b7c908), per_organ_scores.csv (a6c29476f99e).
Arguments
| purpose | Make the per-organ score table and figure for the Otsu run. |
| does | Opens the per-image scores of the Otsu run, computes per organ the image count, found and truth counts, mean and standard deviation of Dice and F1, saves a table (per_organ_scores.csv) and a bar figure. |
| code | import pandas as pd, matplotlib; matplotlib.use('Agg'); import matplotlib.pyplot as plt
from ga_api import save_table, save_figure
p=pd.read_csv('count_objects-5/per_image.csv')
g=p.groupby('group').agg(n_images=('image','count'),found_objects=('n_objects','sum'),truth_objects=('n_truth','sum'),
mean_dice=('dice','mean'),sd_dice=('dice','std'),mean_f1_iou_50=('f1_iou_50','mean'),sd_f1_iou_50=('f ... |
Tool output
group n_images found_objects truth_objects mean_dice sd_dice mean_f1_iou_50 sd_f1_iou_50 mean_average_f1 found_to_truth_ratio
human umbilical cord 11 1355 117 0.092 0.052 0.005 0.008 0.001 11.581
mouse fat (white and brown)_subscapula 42 6405 557 0.103 0.091 0.009 0.017 0.002 11.499
mouse muscle_tibia 28 3530 172 0.103 0.073 0.001 0.005 0.000 20.523
human brain 12 1165 150 0.132 0.052 0.017 0.031 0.005 7.767
mouse heart 28 5930 743 0.201 0.077 0.006 0.008 0.001 7.981
human muscle 9 1228 137 0.259 0.195 0.035 0.076 0.011 8.964
mouse liver 36 7732 674 0.373 0.067 0.034 0.022 0.009 11.472
mouse kidney 40 6327 1625 0.437 0.104 0.063 0.050 0.015 3.894
human melanoma 12 649 584 0.507 0.116 0.171 0.089 0.062 1.111
human testis 12 1257 406 0.527 0.162 0.140 0.116 0.046 3.096
human spleen 34 1959 3876 0.541 0.310 0.170 0.136 0.060 0.505
human peritoneum 12 495 511 0.558 0.076 0.102 0.036 0.038 0.969
human rectum 12 819 390 0.589 0.154 0.138 0.109 0.048 2.100
human liver 40 5875 1471 0.593 0.102 0.100 0.059 0.030 3.994
human cerebellum 12 399 653 0.593 0.396 0.418 0.366 0.247 0.611
human tonsile 12 921 1166 0.607 0.097 0.117 0.047 0.043 0.790
human lung 11 301 364 0.634 0.277 0.278 0.279 0.13
... (1000 more characters in the session record)The model runs calculate.
{"items":[{"name":"found_to_truth_otsu","expression":"ratio(66610, 35138)"},{"name":"dice_range_low","expression":"min([0.5381,0.4700,0.5118,0.5313,0.3552,0.3051])"},{"name":"dice_range_high","expression":"max([0.5381,0.4700,0.5118,0.5313,0.3552,0.3051])"},{"name":"f1_range_low","expression":"min([0.1480,0.0657,0.1059,0.1346,0.1339,0.1088])"},{"name":"f1_range_high","expression":"max([0.1480,0.0657,0.1059,0.1346,0.1339,0.1088])"}]}step n19 calculate
Calculated 5 values.
Arguments
| items | [{"name":"found_to_truth_otsu","expression":"ratio(66610, 35138)"},{"name":"dice_range_low","expression":"min([0.5381,0.4700,0.5118,0.5313,0.3552,0.3051])"},{"name":"dice_range_high","expression":"max([0.5381,0.4700,0.5118,0.5313,0.3552,0.3051])"},{"name":"f1_range_low","expression":"min([0.1480,0.0657,0.1059,0.1346,0.1339,0.1088])"},{"name":"f1_range_high","expression":"max([0.1480,0.0657,0.1059, ... |
Tool output
found_to_truth_otsu = ratio(66610, 35138) = 1.895668507 dice_range_low = min([0.5381,0.4700,0.5118,0.5313,0.3552,0.3051]) = 0.3051 dice_range_high = max([0.5381,0.4700,0.5118,0.5313,0.3552,0.3051]) = 0.5381 f1_range_low = min([0.1480,0.0657,0.1059,0.1346,0.1339,0.1088]) = 0.0657 f1_range_high = max([0.1480,0.0657,0.1059,0.1346,0.1339,0.1088]) = 0.148
plan The model updates its plan.
- done Inspect the folder, the tissue images and the label masks
- done Get the threshold method and the smallest object size from the scientist
- done Outline the nuclei in all 665 tissue images (hematoxylin channel) and score each image against its label mask
- done Compare other threshold methods as a range check
- done Summarize Dice and F1 per organ and overall, with a figure and tables
Model Answer draft 1. The claim check and the review read it.
I outlined the nuclei in all 665 patches from the 31 organ folders and scored each patch against its hand-drawn mask. The outlines cover the right tissue areas only in part, and they match single nuclei poorly. The mean pixel overlap (Dice) is 0.538. The mean object match (F1 at overlap 0.5) is only 0.148.
What I did
- Data check (inspect_image): The tissue images are 512 × 512 pixel RGB PNG files, 8-bit, with no saturated pixels. The label masks are 16-bit TIFF files with one number for each nucleus, so the different bit depth is correct. The 2344 × 1172 pixel files are the vague-area overlay pictures, and I did not use them.
- Outlines (count_objects, scikit-image 0.26):
- Channel: hematoxylin, with nuclei taken as bright.
- Threshold: Otsu, one cutoff for each image.
- Smallest object: 30 pixels, which you approved.
- Objects at the image border: kept.
- Watershed split of touching nuclei: off.
- Sizes are in pixels because no pixel size was given.
- Score: count_objects compared each image with its own file in "label masks". The vague areas were not removed, so they are part of the score. One image is one replicate (n = 665 images).
- Checks: I ran the other threshold methods and a watershed split as comparison runs only. Your settings did not change.
Results (Otsu run, count_objects)
| Measure | Value |
|---|---|
| Images scored | 665 |
| Hand-drawn nuclei | 35138 |
| Objects found | 66610 (1.90 × the hand-drawn count, from calculate) |
| Mean Dice | 0.538 (median 0.643) |
| Mean F1 at IoU 0.5 | 0.148 |
| Mean F1 over IoU 0.5 to 0.95 | 0.058 |
- Dice measures pixel overlap only. It does not show if the object count is correct.
- F1 counts a nucleus as found only if one outline overlaps it by at least half, as intersection over union (IoU).
By organ (per_organ_scores.csv, figure per_organ_dice_f1.png):
- Best Dice: human placenta 0.780, human kidney 0.779, mouse spleen 0.749 and human pancreas 0.746.
- Worst Dice: human umbilical cord 0.092, mouse fat 0.103, mouse muscle_tibia 0.103 and human brain 0.132. In these organs the run finds 7.8 to 20.5 times more objects than the masks hold.
- Dense organs (mouse spleen 0.323, mouse thymus 0.363, human kidney 0.368) give high Dice, but the run finds fewer objects than the masks hold.
- Highest F1 at IoU 0.5: human cerebellum 0.418 and human pylorus 0.399.
Comparison runs (compare_options, method_comparison.csv):
| Method | Objects found | Mean Dice | Mean F1 at IoU 0.5 |
|---|---|---|---|
| Otsu | 66610 | 0.538 | 0.148 |
| isodata | 66621 | 0.531 | 0.135 |
| li | 65132 | 0.512 | 0.106 |
| mean | 76603 | 0.470 | 0.066 |
| triangle | 16395 | 0.355 | 0.134 |
| yen | 16574 | 0.305 | 0.109 |
- Across these methods, Dice goes from 0.305 to 0.538 and F1 from 0.066 to 0.148.
- The "minimum" method gave no result in the comparison, so I do not report it.
- With the watershed split on (Otsu), the run found 295536 objects. Dice was 0.5385 and F1 at IoU 0.5 fell to 0.094.
What I infer
- These results hold for all the methods I tried: F1 is low (0.15 or less) and the object counts do not match the masks.
- Which method gives the best Dice depends on the method. Otsu is the best of those tested.
- I think there are two failure types:
- In organs with few nuclei, one cutoff for each image also marks stroma, muscle or cytoplasm, which gives many false objects.
- In dense organs, touching nuclei merge into one object, which gives high Dice but few matches.
- I did not confirm these causes by looking at the outline images.
What is uncertain
- The vague areas are inside the score. If you remove them, the scores can change.
- I matched each comparison run to its method by its object count, not by a file label.
Decisions for you
- Do you want me to score again with the vague areas removed?
- Do you want me to test a larger smallest-object size for the organs with few nuclei?
- A plain threshold cannot give a good match for each nucleus here. That needs a trained nucleus segmentation method, which this program does not include.
Files are in the session folder:
- count_objects-5/per_image.csv
- count_objects-5/per_group.csv
- per_organ_scores.csv
- method_comparison.csv
- figures/per_organ_dice_f1.png and .svg
claim check 71 numbers in the answer, 2 numbers without a source in the record.
The review found 2 problems. The model corrects the answer before the scientist sees it.
review The review sent the answer back to the model before the scientist saw it.
- The answer uses 0.368, 0.512, 0.066, 0.355, 0.066, 0.15 from a comparison run of another option (threshold_method), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison. - 2 numbers in the answer match no logged tool result: 0.95, 0.323. Run the tool that measures each number, or remove the number, or say how you derived it from logged results. Fix these problems. Then give the answer again.
Model The model writes its answer. The claim check reads it. See the final answer.
claim check 71 numbers in the answer, 0 numbers without a source in the record.
claim check 71 numbers in the answer, 0 numbers without a source in the record.
Saved: results.xlsx (12 sheets), 1 figure in figures/. Folder: {work}
The final answer
The model wrote this answer. The harness shows it to the scientist after the review.
I outlined the nuclei in all 665 patches from the 31 organ folders and scored each patch against its hand-drawn mask. With your chosen settings, the outlines cover the right tissue areas only in part, and they match single nuclei poorly. The mean pixel overlap (Dice) is 0.538. The mean object match (F1 at an overlap of 0.5) is only 0.148.
What I did
- Data check (inspect_image): The tissue images are 512 × 512 pixel RGB PNG files, 8-bit, with no saturated pixels. The label masks are 16-bit TIFF files with one number for each nucleus, so the different bit depth is correct. The 2344 × 1172 pixel files are the vague-area overlay pictures, and I did not use them.
- Outlines (count_objects, scikit-image 0.26):
- Channel: hematoxylin, with nuclei taken as bright.
- Threshold: Otsu, one cutoff for each image.
- Smallest object: 30 pixels, which you approved.
- Objects at the image border: kept.
- Watershed split of touching nuclei: off.
- Sizes are in pixels because no pixel size was given.
- Score: count_objects compared each image with its own file in "label masks". The vague areas were not removed, so they are part of the score. One image is one replicate (n = 665 images).
- Checks: I ran the other threshold methods and a watershed split as comparison runs only. Your settings did not change.
Results with the chosen setting (Otsu run, count_objects)
| Measure | Value |
|---|---|
| Images scored | 665 |
| Hand-drawn nuclei | 35138 |
| Objects found | 66610 (1.90 × the hand-drawn count, from calculate) |
| Mean Dice | 0.538 (median 0.643) |
| Mean F1 at IoU 0.5 | 0.148 |
| Mean average F1 | 0.058 |
- Dice measures pixel overlap only. It does not show if the object count is correct.
- F1 counts a nucleus as found only if one outline overlaps it by at least half, as intersection over union (IoU).
- Average F1 is the F1 averaged over a range of stricter IoU cutoffs, as the count_objects tool defines it.
By organ, same Otsu run (per_organ_scores.csv, figure per_organ_dice_f1.png):
- Best Dice: human placenta 0.780, human kidney 0.779, mouse spleen 0.749 and human pancreas 0.746.
- Worst Dice: human umbilical cord 0.092, mouse fat 0.103, mouse muscle_tibia 0.103 and human brain 0.132. In these organs the run finds 7.8 to 20.5 times more objects than the masks hold.
- Dense organs give high Dice, but the run finds too few objects:
- mouse spleen: 656 found against 2029 hand-drawn.
- mouse thymus: 614 found against 1691 hand-drawn.
- human kidney: 559 found against 1521 hand-drawn.
- Highest F1 at IoU 0.5: human cerebellum 0.418 and human pylorus 0.399.
Comparison runs only (compare_options; these are not the chosen setting)
Each row is a check run with one other threshold method. The other settings stayed the same.
| Method (comparison) | Objects found | Mean Dice | Mean F1 at IoU 0.5 |
|---|---|---|---|
| isodata | 66621 | 0.531 | 0.135 |
| li | 65132 | 0.512 | 0.106 |
| mean | 76603 | 0.470 | 0.066 |
| triangle | 16395 | 0.355 | 0.134 |
| yen | 16574 | 0.305 | 0.109 |
- Across Otsu and these comparison methods, Dice goes from 0.305 to 0.538 and F1 at IoU 0.5 from 0.0657 to 0.148 (from calculate).
- The "minimum" method gave no result in the comparison, so I do not report it.
- A comparison run with the watershed split on (Otsu) found 295536 objects. Dice was 0.5385 and F1 at IoU 0.5 fell to 0.094.
What I infer
- These results hold for all the methods I tried: F1 at IoU 0.5 is 0.148 or less, and the object counts do not match the masks.
- Which method gives the best Dice depends on the method. Otsu is the best of those tested.
- I think there are two failure types:
- In organs with few nuclei, one cutoff for each image also marks stroma, muscle or cytoplasm, which gives many false objects.
- In dense organs, touching nuclei merge into one object, which gives high Dice but few matches.
- I did not confirm these causes by looking at the outline images.
What is uncertain
- The vague areas are inside the score. If you remove them, the scores can change.
- I matched each comparison run to its method by its object count, not by a file label.
Decisions for you
- Do you want me to score again with the vague areas removed?
- Do you want me to test a larger smallest-object size for the organs with few nuclei?
- A plain threshold cannot give a good match for each nucleus here. That needs a trained nucleus segmentation method, which this program does not include.
Files are in the session folder:
- count_objects-5/per_image.csv
- count_objects-5/per_group.csv
- per_organ_scores.csv
- method_comparison.csv
- figures/per_organ_dice_f1.png and .svg
Settings and step results that the answer does not mention. The harness adds them.
Step results that the answer does not mention, from the record:
- n10 run_script: The script ran in {work} and wrote 0 new file(s) to {work}.
- n16 run_script: The script ran in {work} and wrote 1 new file(s) to {work}.
- n18 run_script: The script ran in {work} and wrote 3 new file(s) to {work}.
Settings used, from the decision record: Pixel size in micrometers (0 = uncalibrated): 0 · Image channel to segment: hematoxylin · Are the objects bright or dark in that channel?: bright · Threshold method: otsu · Smallest object to count (pixels): 30 · Remove objects that touch the image border: false · Split touching objects with a watershed: false.Checks
Review findings
The review recorded 10 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.
| Severity | From | Finding | Shown with the final answer |
|---|---|---|---|
| error | rulenumber_from_comparison | The answer uses 0.512, 0.066, 0.355 from a comparison run of another option (threshold_method), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison. | yes |
| warning | ruleborder_objects_kept | Objects that touch the border are in the count. Report the count with and without them. | yes |
| info | ruletext_style | The answer breaks the text rules (ASD-STE100) in 3 places. Sentence 14 uses the passive voice: "was given". Use the active voice. Sentence 16 uses the passive voice: "were not removed". Use the active voice. Sentence 39 has 26 words. The limit is 25. | yes |
| warning | referee model | The object count changes by much more than 20 percent between threshold methods: 16395 with triangle and 76603 with mean. The report shows these counts in a table, but it must state in words that the count depends strongly on the method. | yes |
| warning | referee model | The statement that a plain threshold cannot give a good match for each nucleus is stronger than the evidence. The log tests only global thresholds with one size filter of 30 pixels. It does not test local thresholds or other size filters. | yes |
| info | referee model | compare_options received six methods, but only five comparison runs occurred. The 'minimum' method gave no result and the log shows no error message. The report says that minimum gave no result. | yes |
| info | referee model | The analyst matched comparison runs to threshold methods by object count, not by a run label. The triangle and yen counts are close (16395 and 16574). The mapping in the log is consistent, and the report states this limit. | yes |
| info | referee model | The two causes of failure (stroma marked as nuclei, and touching nuclei that merge) are inferences. The log has no outline images to check them. The report states that the causes are not confirmed. | yes |
| info | referee model | The log does not agree with itself about the threshold method. At step 8 the record already says otsu, but the later decision records a change from null to otsu. The final setting is otsu, and the scientist chose it. | yes |
| info | referee model | The score includes the vague areas, and the report says so. All sizes are in pixels because the pixel size is 0. The report gives no p-value, so there is no pseudoreplication. The unit of n is the image (n = 665). | yes |
Numbers in the answer
The last claim check read 71 numbers in the answer. 71 numbers match a logged result. 0 numbers have no source in the record.
Deviations
The model did not try to change a choice of the scientist.
Failed tool calls
No tool call failed.
Data integrity
Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.
| File | SHA-256 | Fetched data | Steps with this hash |
|---|---|---|---|
{data}/mahbod2024-nuinsseg/x128.0 KB | - | file not found or too large to hash | none |
A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.
How to repeat it
Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.
CUVETTE_DATA={data} bash bench/papers/mahbod2024-nuinsseg/fetch.shRun the same case with Cuvette. The script gives the same answers from bench/papers/mahbod2024-nuinsseg/bench.yaml.
cuvette bench papers --papers mahbod2024-nuinsseg --models claude:claude-opus-5-5
Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.
inspect_image(step n1)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x")The manual route gives the same numbers. An automatic test in Cuvette checks this.
inspect_image(step n2)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png")The manual route gives the same numbers. An automatic test in Cuvette checks this.
inspect_image(step n3)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif")The manual route gives the same numbers. An automatic test in Cuvette checks this.
inspect_image(step n4)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x/human bladder/vague areas/overlay_save_path/human_bladder_01.png
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x/human bladder/vague areas/overlay_save_path/human_bladder_01.png")The manual route gives the same numbers. An automatic test in Cuvette checks this.
count_objects(step n9)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
run_script(step n10)Run the Python code in {work}/script-1/script.py
- Code only: this step has no route in the program menus. Run it with the script or flow export.
The program has no menu route for this step. To repeat it, run the code.
run_script(step n16)Run the Python code in {work}/script-2/script.py
- Code only: this step has no route in the program menus. Run it with the script or flow export.
The program has no menu route for this step. To repeat it, run the code.
run_script(step n18)Run the Python code in {work}/script-3/script.py
- Code only: this step has no route in the program menus. Run it with the script or flow export.
The program has no menu route for this step. To repeat it, run the code.
calculate(step n19)Run the tool "calculate" with these settings: {"items":[{"name":"found_to_truth_otsu","expression":"ratio(66610, 35138)"},{"name":"dice_range_low","expression":"min([0.5381,0.4700,0.5118,0.5313,0.3552,0.3051])"},{"name":"dice_range_high","expression":"max([0.5381,0.4700,0.5118,0.5313,0.3552,0.3051])"},{"name":"f1_range_low","expression":"min([0.1480,0.0657,0.1059,0.1346,0.1339,0.1088])"},{"name":"f1_range_high","expression":"max([0.1480,0.0657,0.1059,0.1346,0.1339,0.1088])"}]}. - Code only: this step has no route in the program menus. Run it with the script or flow export.The harness recorded no manual route for this step.
Figure
Run facts
| Model | claude-opus-5-5 through the Anthropic service |
| Date | 2026-10-09 13:35:32 UTC |
| End of run | the model gave a final answer |
| Time | 564 s |
| Requests to the model | 15 |
| Tokensunits of text that the model read and wrote | 34 input, 9932 output, 254502 cache read, 31463 cache write |
| Cost estimate | $0.41 at list price, from the token counts |
| Tool calls | 18 (0 failed) |
| Adapters | scikit-image 0.1.1, program 0.26.0 |
| Session | 20261009-083532-b4bc |
Code hash of each step (19)
| Step | Tool | Program version | Code hash |
|---|---|---|---|
| n1 | inspect_image | 0.26.0 | cd246d8739be |
| n2 | inspect_image | 0.26.0 | cd246d8739be |
| n3 | inspect_image | 0.26.0 | cd246d8739be |
| n4 | inspect_image | 0.26.0 | cd246d8739be |
| n5 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n6 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n7 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n8 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n9 | count_objects | 0.26.0 | a909f01092a4 |
| n10 | run_script | - | 995d74a3af3a |
| n11 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n12 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n13 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n14 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n15 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n16 | run_script | - | 995d74a3af3a |
| n17 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n18 | run_script | - | 995d74a3af3a |
| n19 | calculate | - | d864d37ef90b |
The code hash is a fingerprint of the adapter name, the adapter version, the tool and its definition in the adapter. If one of these changes, the hash changes.
Sonnet · claude-sonnet-5-5 · run 3 of 3 shown 3 of 3 values match, 1 of 1 correct in the final answer
The session
This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.
setup The decision record starts with these values. A script set them before the first message, from the answers that we wrote before the run.
- Image channel for the nuclei: hematoxylinWhere the answer comes from: Not in the paper. The paper trains its models on the RGB images. Hematoxylin stains the nuclei, so we use that channel.
- Are the objects bright or dark in the channel: brightWhere the answer comes from: Not in the paper. Nuclei have high values in the hematoxylin channel after color deconvolution.
- Remove objects that touch the image edge: falseWhere the answer comes from: Not in the paper. We keep the objects at the patch edge.
- Split touching nuclei: falseWhere the answer comes from: Not in the paper for a threshold method. Our baseline does no declumping.
- Pixel size: 0Where the answer comes from: Not in the paper text. The paper names a 40x objective with numerical aperture 0.75 but gives no micrometers per pixel.
- Unit of replication: images or fieldsWhere the answer comes from: Technical Validation section. The paper splits the patches into five folds of 133 images each.
harness note A note from the harness to the model. The scientist did not write it.
- Setup values: Design: - What is the unit of replication? (replication_unit): images or fields - Pixel size in micrometers (0 = uncalibrated) (pixel_size): 0 Segmentation: - Image channel to segment (stain_channel): hematoxylin - Are the objects bright or dark in that channel? (object_polarity): bright - Remove objects that touch the image border (exclude_border_objects): false - Split touching objects with a watershed (split_touching): false Ask the scientist: Threshold method (threshold_method), Smallest object to count (pixels) (min_object_size). The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist. - Mode is now watch: you run the steps.
The model reads the method notes
score-against-truth.The model calls
inspect_image (adapter scikit-image).step n1 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
Folder with 3325 image files in 5 pattern(s). */label masks/*.tif: 665 files, for example human bladder/label masks/human_bladder_01.tif; */mask binary/*.png: 665 files, for example human bladder/mask binary/human_bladder_01.png; */tissue images/*.png: 665 files, for example human bladder/tissue images/human_bladder_01.png; */vague areas/mask binary/*.png: 665 files, for example human bladder/vague areas/mask binary/human_bladder_01.png; */vague areas/overlay_save_path/*.png: 665 files, for example human bladder/vague areas/overlay_save_path/human_bladder_01.png
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
Tool output
{
"ok": true,
"summary": "Folder with 3325 image files in 5 pattern(s). */label masks/*.tif: 665 files, for example human bladder/label masks/human_bladder_01.tif; */mask binary/*.png: 665 files, for example human bladder/mask binary/human_bladder_01.png; */tissue images/*.png: 665 files, for example human bladder/tissue images/human_bladder_01.png; */vague areas/mask binary/*.png: 665 files, for example human bladder/vague areas/mask binary/human_bladder_01.png; */vague areas/overlay_save_path/*.png: 665 files, for example human bladder/vague areas/overlay_save_path/human_bladder_01.png",
"metrics": {
"n_files": 3325,
"n_patterns": 5
},
"data": {
"patterns": {
"*/label masks/*.tif": {
"n_files": 665,
"example": "human bladder/label masks/human_bladder_01.tif"
},
"*/mask binary/*.png": {
"n_files": 665,
"example": "human bladder/mask binary/human_bladder_01.png"
},
"*/tissue images/*.png": {
"n_files": 665,
"example": "human bladder/tissue images/human_bladder_01.png"
},
"*/vague areas/mask binary/*.png": {
"n_files": 665,
"example": "human bladder/vague areas/mask binary/human_bladder_01.png"
},
"*/vague areas/overlay_save_path/*.png": {
"n_files": 665,
"example": "human bladder/vague areas/overlay_save_path/human_bladder_01.png"
}
},
"note": "Give the folder as the path and one pattern to the segmentation tool. Give the folder of the hand-drawn masks as truth_folder and a part of its path as truth_match."
},
"outputs": []
}Model thinking, summary: The bit depth difference makes sense now—masks are 16-bit, images are 8-bit. The 2344x1172 dimensions are likely the overlay images, so I should check one of the tissue images to confirm.
The model calls
inspect_image (adapter scikit-image).step n2 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
512 by 512 pixels, 3 channel(s), uint8, min 14, max 174
Input file: {data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png SHA-256 bc59dd2d355a.
Arguments
| path | {data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png |
Tool output
{
"ok": true,
"summary": "512 by 512 pixels, 3 channel(s), uint8, min 14, max 174",
"metrics": {
"width": 512,
"height": 512,
"channels": 3,
"bit_depth": 8,
"min_value": 14,
"max_value": 174,
"mean_value": 106.69461186726888,
"saturated_fraction": 0
},
"data": {
"dtype": "uint8",
"warnings": [],
"calibrated": false
},
"outputs": []
}The model calls
inspect_image (adapter scikit-image).step n3 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
512 by 512 pixels, 1 channel(s), uint16, min 0, max 64
Input file: {data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif SHA-256 9ae5584ca3c5.
Arguments
| path | {data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif |
Tool output
{
"ok": true,
"summary": "512 by 512 pixels, 1 channel(s), uint16, min 0, max 64",
"metrics": {
"width": 512,
"height": 512,
"channels": 1,
"bit_depth": 16,
"min_value": 0,
"max_value": 64,
"mean_value": 1.8787879943847656,
"saturated_fraction": 0
},
"data": {
"dtype": "uint16",
"warnings": [],
"calibrated": false
},
"outputs": []
}Model thinking, summary: That clears up the bit depth mismatch - the tissue images and masks just use different formats, which isn't a problem. Now I need to check with the scientist on the threshold method and minimum size settings before proceeding.
The model calls
ask_scientist.decision card Which threshold method and smallest object size (pixels) do you want for the ...
Which threshold method and smallest object size (pixels) do you want for the nuclei outlines?
Options: otsu, 30 pixels li, 30 pixels triangle, 30 pixels otsu, 0 pixels
Suggested: otsu with 30 pixels as the main run. I then compare li, triangle, isodata and yen as a range. (The tissue images are 8-bit 512 x 512 H&E patches. The mixed bit depth in the folder is only masks (16-bit) against images (8-bit), so it does not affect the segmentation. The method changes the count, so I report a range.)
Answer otsu with 30 pixels as the main run. I then compare li, triangle, isodata and yen as a range.
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper.
Result of ask_scientist: The scientist answered: otsu with 30 pixels as the main run. I then compare li, triangle, isodata and yen as a range. (a script gave the answer that we wrote before the run)
Model
Next I run the main segmentation on all organ folders and score it against the label masks.
The model calls
count_objects (adapter scikit-image).paused The harness paused count_objects until the scientist chose: Threshold method, Smallest object to keep. The decision cards follow.
Comparing the options for "Threshold method" before it asks the scientist.
comparison run n4 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 1894955 objects; mean Dice 0.491 over 665 scored images
Outputs: per_group.csv (853bd10b304e), per_image.csv (9bccd8c00eb4).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 0 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 1894955 objects; mean Dice 0.491 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 1894955,
"total_objects": 1894955,
"mean_objects_per_image": 2849.5563909774437,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.4908461654135338,
"mean_f1_iou_50": 0.009448120300751879,
"mean_average_f1": 0.003823458646616541
},
"outputs": [
{
"path": "{work}/count_objects-1/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-1/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
27020,
2251.67,
453,
0.642
],
[
"human brain",
12,
33108,
2759,
150,
0.1121
],
[
"human cardia",
12,
20820,
1735,
767,
0.6844
],
[
"human cerebellum",
12,
6969,
580.75,
653,
0.5901
],
[
"human epiglottis",
11,
9933,
903,
261,
0.6153
],
[
"human jejunum",
10,
37063,
3706.3,
1033,
0.684
],
[
"human kidney",
11,
19265,
1751.36,
1521,
0.7819
],
[
"human liver",
40,
192526,
4813.15,
1471,
0.4819
],
[
"human lung",
11,
9098,
827.09,
364,
0.6118
],
[
"human melanoma",
12,
28400,
2366.67,
584,
0.4572
],
[
"human muscle",
9,
33221,
3691.22,
137,
0.2146
],
[
"human oesophagus",
47,
158641,
3375.34,
2329,
0.5954
],
[
"human pancreas",
44,
48638,
1105.41,
2436,
0.731
],
[
"human peritoneum",
12,
28391,
2365.92,
511,
0.5126
],
[
"human placenta",
40,
87267,
2181.68,
2415,
0.7457
],
[
"human pylorus",
12,
27721,
2310.08,
481,
0.6612
],
[
"human rectum",
12,
36398,
3033.17,
390,
0.4987
],
[
"human salivory gland",
44,
139296,
3165.82,
3577,
0.6761
],
[
"human spleen",
34,
55998,
1647,
3876,
0.5216
],
[
"human testis",
12,
42126,
3510.5,
406,
0.4487
],
[
"human tongue",
40,
77142,
1928.55,
1628,
0.6861
],
[
"human tonsile",
12,
25643,
2136.92,
1166,
0.5867
],
[
"human umbilical cord",
11,
43744,
3976.73,
117,
0.0698
],
[
"mouse fat (white and brown)_subscapula",
42,
103878,
2473.29,
557,
0.0863
],
[
"mouse femur",
6,
26960,
4493.33,
921,
0.6345
],
[
"mouse heart",
28,
106172,
3791.86,
743,
0.
... (504 more characters in the session record)comparison run n5 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 1902977 objects; mean Dice 0.463 over 665 scored images
Outputs: per_group.csv (c9c58464d3de), per_image.csv (2f8ae176a867).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | li |
| min_object_size | 0 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 1902977 objects; mean Dice 0.463 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 1902977,
"total_objects": 1902977,
"mean_objects_per_image": 2861.6195488721805,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.46283774436090225,
"mean_f1_iou_50": 0.006028721804511279,
"mean_average_f1": 0.002441203007518797
},
"outputs": [
{
"path": "{work}/count_objects-2/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-2/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
44207,
3683.92,
453,
0.4735
],
[
"human brain",
12,
27083,
2256.92,
150,
0.0982
],
[
"human cardia",
12,
33933,
2827.75,
767,
0.5907
],
[
"human cerebellum",
12,
20249,
1687.42,
653,
0.6005
],
[
"human epiglottis",
11,
21610,
1964.55,
261,
0.4201
],
[
"human jejunum",
10,
36186,
3618.6,
1033,
0.6813
],
[
"human kidney",
11,
22810,
2073.64,
1521,
0.8101
],
[
"human liver",
40,
156081,
3902.03,
1471,
0.4576
],
[
"human lung",
11,
19577,
1779.73,
364,
0.5233
],
[
"human melanoma",
12,
33269,
2772.42,
584,
0.3831
],
[
"human muscle",
9,
29158,
3239.78,
137,
0.1881
],
[
"human oesophagus",
47,
148629,
3162.32,
2329,
0.5756
],
[
"human pancreas",
44,
59558,
1353.59,
2436,
0.6984
],
[
"human peritoneum",
12,
29395,
2449.58,
511,
0.4561
],
[
"human placenta",
40,
110605,
2765.12,
2415,
0.7334
],
[
"human pylorus",
12,
47648,
3970.67,
481,
0.5079
],
[
"human rectum",
12,
37697,
3141.42,
390,
0.4015
],
[
"human salivory gland",
44,
157550,
3580.68,
3577,
0.6595
],
[
"human spleen",
34,
74901,
2202.97,
3876,
0.6595
],
[
"human testis",
12,
40114,
3342.83,
406,
0.3846
],
[
"human tongue",
40,
123550,
3088.75,
1628,
0.5773
],
[
"human tonsile",
12,
22544,
1878.67,
1166,
0.56
],
[
"human umbilical cord",
11,
38989,
3544.45,
117,
0.0581
],
[
"mouse fat (white and brown)_subscapula",
42,
80107,
1907.31,
557,
0.0802
],
[
"mouse femur",
6,
28469,
4744.83,
921,
0.634
],
[
"mouse heart",
28,
77945,
2783
... (524 more characters in the session record)comparison run n6 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 1891610 objects; mean Dice 0.485 over 665 scored images
Outputs: per_group.csv (e332e4808dbc), per_image.csv (9d52adf9c7be).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | isodata |
| min_object_size | 0 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 1891610 objects; mean Dice 0.485 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 1891610,
"total_objects": 1891610,
"mean_objects_per_image": 2844.5263157894738,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.48485353383458646,
"mean_f1_iou_50": 0.008556992481203007,
"mean_average_f1": 0.0033469172932330824
},
"outputs": [
{
"path": "{work}/count_objects-3/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-3/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
30953,
2579.42,
453,
0.6055
],
[
"human brain",
12,
31209,
2600.75,
150,
0.1081
],
[
"human cardia",
12,
22368,
1864,
767,
0.674
],
[
"human cerebellum",
12,
21197,
1766.42,
653,
0.4059
],
[
"human epiglottis",
11,
15490,
1408.18,
261,
0.5272
],
[
"human jejunum",
10,
36985,
3698.5,
1033,
0.6839
],
[
"human kidney",
11,
19477,
1770.64,
1521,
0.7874
],
[
"human liver",
40,
184734,
4618.35,
1471,
0.476
],
[
"human lung",
11,
12064,
1096.73,
364,
0.5021
],
[
"human melanoma",
12,
29470,
2455.83,
584,
0.4375
],
[
"human muscle",
9,
32003,
3555.89,
137,
0.2105
],
[
"human oesophagus",
47,
153020,
3255.74,
2329,
0.5925
],
[
"human pancreas",
44,
50490,
1147.5,
2436,
0.7266
],
[
"human peritoneum",
12,
29086,
2423.83,
511,
0.501
],
[
"human placenta",
40,
88119,
2202.97,
2415,
0.7452
],
[
"human pylorus",
12,
31126,
2593.83,
481,
0.6312
],
[
"human rectum",
12,
37716,
3143,
390,
0.4702
],
[
"human salivory gland",
44,
143005,
3250.11,
3577,
0.6741
],
[
"human spleen",
34,
66112,
1944.47,
3876,
0.6567
],
[
"human testis",
12,
41867,
3488.92,
406,
0.4386
],
[
"human tongue",
40,
86933,
2173.32,
1628,
0.6636
],
[
"human tonsile",
12,
25208,
2100.67,
1166,
0.5825
],
[
"human umbilical cord",
11,
38272,
3479.27,
117,
0.0653
],
[
"mouse fat (white and brown)_subscapula",
42,
90819,
2162.36,
557,
0.0833
],
[
"mouse femur",
6,
27561,
4593.5,
921,
0.6341
],
[
"mouse heart",
28,
96157,
3434.18,
... (516 more characters in the session record)comparison run n7 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 620691 objects; mean Dice 0.387 over 665 scored images
Outputs: per_group.csv (9f42adba2e98), per_image.csv (27b89957f986).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | triangle |
| min_object_size | 0 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 620691 objects; mean Dice 0.387 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 620691,
"total_objects": 620691,
"mean_objects_per_image": 933.36992481203,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.3868984962406015,
"mean_f1_iou_50": 0.009778646616541351,
"mean_average_f1": 0.0035595488721804507
},
"outputs": [
{
"path": "{work}/count_objects-4/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-4/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
8166,
680.5,
453,
0.6689
],
[
"human brain",
12,
9979,
831.58,
150,
0.5013
],
[
"human cardia",
12,
8445,
703.75,
767,
0.5234
],
[
"human cerebellum",
12,
5223,
435.25,
653,
0.7214
],
[
"human epiglottis",
11,
3540,
321.82,
261,
0.7002
],
[
"human jejunum",
10,
10113,
1011.3,
1033,
0.1601
],
[
"human kidney",
11,
9940,
903.64,
1521,
0.2134
],
[
"human liver",
40,
52161,
1304.03,
1471,
0.2584
],
[
"human lung",
11,
4540,
412.73,
364,
0.6691
],
[
"human melanoma",
12,
8978,
748.17,
584,
0.4307
],
[
"human muscle",
9,
9558,
1062,
137,
0.4741
],
[
"human oesophagus",
47,
53806,
1144.81,
2329,
0.3458
],
[
"human pancreas",
44,
21176,
481.27,
2436,
0.388
],
[
"human peritoneum",
12,
9736,
811.33,
511,
0.2917
],
[
"human placenta",
40,
41631,
1040.78,
2415,
0.635
],
[
"human pylorus",
12,
8887,
740.58,
481,
0.6739
],
[
"human rectum",
12,
10360,
863.33,
390,
0.6017
],
[
"human salivory gland",
44,
48251,
1096.61,
3577,
0.3354
],
[
"human spleen",
34,
23066,
678.41,
3876,
0.2216
],
[
"human testis",
12,
12397,
1033.08,
406,
0.4861
],
[
"human tongue",
40,
27942,
698.55,
1628,
0.5987
],
[
"human tonsile",
12,
10010,
834.17,
1166,
0.1283
],
[
"human umbilical cord",
11,
9771,
888.27,
117,
0.4629
],
[
"mouse fat (white and brown)_subscapula",
42,
43225,
1029.17,
557,
0.2551
],
[
"mouse femur",
6,
9216,
1536,
921,
0.2877
],
[
"mouse heart",
28,
29892,
1067.57,
743,
0.3313
],
[
"mouse
... (475 more characters in the session record)comparison Comparison runs for Threshold method. The record keeps the scientist's choice.
Threshold method n_objects Result otsu 1894955 ok li 1902977 ok isodata 1891610 ok triangle 620691 ok
decision card Threshold method
The threshold decides which pixels are objects. Different methods can give different counts. The model runs the main methods and shows you the counts before you choose. The model wants to run count_objects.
Options: otsu li triangle isodata yen mean minimum
Suggested: otsu (The model proposed this value when it asked to run the step.)
Data that the model gave for this card
Threshold method n_objects Result otsu 1894955 ok li 1902977 ok isodata 1891610 ok triangle 620691 ok n_objects depends on the choice: 1894955 with otsu, 1902977 with li, 1891610 with isodata, 620691 with triangle
Answer otsu
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper uses deep learning. We use a global Otsu threshold as a simple baseline.
Comparing the options for "Smallest object to count (pixels)" before it asks the scientist.
comparison run n8 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 1894955 objects; mean Dice 0.491 over 665 scored images
Outputs: per_group.csv (853bd10b304e), per_image.csv (9bccd8c00eb4).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 0 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 1894955 objects; mean Dice 0.491 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 1894955,
"total_objects": 1894955,
"mean_objects_per_image": 2849.5563909774437,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.4908461654135338,
"mean_f1_iou_50": 0.009448120300751879,
"mean_average_f1": 0.003823458646616541
},
"outputs": [
{
"path": "{work}/count_objects-5/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-5/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
27020,
2251.67,
453,
0.642
],
[
"human brain",
12,
33108,
2759,
150,
0.1121
],
[
"human cardia",
12,
20820,
1735,
767,
0.6844
],
[
"human cerebellum",
12,
6969,
580.75,
653,
0.5901
],
[
"human epiglottis",
11,
9933,
903,
261,
0.6153
],
[
"human jejunum",
10,
37063,
3706.3,
1033,
0.684
],
[
"human kidney",
11,
19265,
1751.36,
1521,
0.7819
],
[
"human liver",
40,
192526,
4813.15,
1471,
0.4819
],
[
"human lung",
11,
9098,
827.09,
364,
0.6118
],
[
"human melanoma",
12,
28400,
2366.67,
584,
0.4572
],
[
"human muscle",
9,
33221,
3691.22,
137,
0.2146
],
[
"human oesophagus",
47,
158641,
3375.34,
2329,
0.5954
],
[
"human pancreas",
44,
48638,
1105.41,
2436,
0.731
],
[
"human peritoneum",
12,
28391,
2365.92,
511,
0.5126
],
[
"human placenta",
40,
87267,
2181.68,
2415,
0.7457
],
[
"human pylorus",
12,
27721,
2310.08,
481,
0.6612
],
[
"human rectum",
12,
36398,
3033.17,
390,
0.4987
],
[
"human salivory gland",
44,
139296,
3165.82,
3577,
0.6761
],
[
"human spleen",
34,
55998,
1647,
3876,
0.5216
],
[
"human testis",
12,
42126,
3510.5,
406,
0.4487
],
[
"human tongue",
40,
77142,
1928.55,
1628,
0.6861
],
[
"human tonsile",
12,
25643,
2136.92,
1166,
0.5867
],
[
"human umbilical cord",
11,
43744,
3976.73,
117,
0.0698
],
[
"mouse fat (white and brown)_subscapula",
42,
103878,
2473.29,
557,
0.0863
],
[
"mouse femur",
6,
26960,
4493.33,
921,
0.6345
],
[
"mouse heart",
28,
106172,
3791.86,
743,
0.
... (504 more characters in the session record)comparison run n9 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 149485 objects; mean Dice 0.527 over 665 scored images
Outputs: per_group.csv (a37a55ad5861), per_image.csv (ba0b17ed4556).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 15 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 149485 objects; mean Dice 0.527 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 149485,
"total_objects": 149485,
"mean_objects_per_image": 224.78947368421052,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5273685714285714,
"mean_f1_iou_50": 0.1104572932330827,
"mean_average_f1": 0.04424015037593985
},
"outputs": [
{
"path": "{work}/count_objects-6/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-6/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
704,
58.67,
453,
0.7236
],
[
"human brain",
12,
2629,
219.08,
150,
0.1266
],
[
"human cardia",
12,
795,
66.25,
767,
0.7234
],
[
"human cerebellum",
12,
649,
54.08,
653,
0.5934
],
[
"human epiglottis",
11,
616,
56,
261,
0.6418
],
[
"human jejunum",
10,
1668,
166.8,
1033,
0.7215
],
[
"human kidney",
11,
854,
77.64,
1521,
0.7816
],
[
"human liver",
40,
16936,
423.4,
1471,
0.5625
],
[
"human lung",
11,
480,
43.64,
364,
0.6304
],
[
"human melanoma",
12,
1257,
104.75,
584,
0.4988
],
[
"human muscle",
9,
2985,
331.67,
137,
0.2474
],
[
"human oesophagus",
47,
14944,
317.96,
2329,
0.6468
],
[
"human pancreas",
44,
3321,
75.48,
2436,
0.7433
],
[
"human peritoneum",
12,
1244,
103.67,
511,
0.5502
],
[
"human placenta",
40,
4884,
122.1,
2415,
0.7745
],
[
"human pylorus",
12,
715,
59.58,
481,
0.734
],
[
"human rectum",
12,
2137,
178.08,
390,
0.57
],
[
"human salivory gland",
44,
6487,
147.43,
3577,
0.7124
],
[
"human spleen",
34,
3141,
92.38,
3876,
0.5381
],
[
"human testis",
12,
2841,
236.75,
406,
0.5099
],
[
"human tongue",
40,
2886,
72.15,
1628,
0.7342
],
[
"human tonsile",
12,
1484,
123.67,
1166,
0.6036
],
[
"human umbilical cord",
11,
3366,
306,
117,
0.0859
],
[
"mouse fat (white and brown)_subscapula",
42,
12811,
305.02,
557,
0.0974
],
[
"mouse femur",
6,
1310,
218.33,
921,
0.6625
],
[
"mouse heart",
28,
14681,
524.32,
743,
0.1822
],
[
"mouse kidney",
40,
16094,
402.35
... (433 more characters in the session record)comparison run n10 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-7/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-7/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (412 more characters in the session record)comparison run n11 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 35538 objects; mean Dice 0.547 over 665 scored images
Outputs: per_group.csv (eadb61857fce), per_image.csv (9ae6cd367f00).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 60 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 35538 objects; mean Dice 0.547 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 35538,
"total_objects": 35538,
"mean_objects_per_image": 53.440601503759396,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5465819548872181,
"mean_f1_iou_50": 0.1782366917293233,
"mean_average_f1": 0.06803609022556391
},
"outputs": [
{
"path": "{work}/count_objects-8/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-8/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
266,
22.17,
453,
0.7364
],
[
"human brain",
12,
642,
53.5,
150,
0.1373
],
[
"human cardia",
12,
372,
31,
767,
0.7307
],
[
"human cerebellum",
12,
271,
22.58,
653,
0.5928
],
[
"human epiglottis",
11,
221,
20.09,
261,
0.6497
],
[
"human jejunum",
10,
396,
39.6,
1033,
0.7288
],
[
"human kidney",
11,
453,
41.18,
1521,
0.7765
],
[
"human liver",
40,
2343,
58.58,
1471,
0.6144
],
[
"human lung",
11,
229,
20.82,
364,
0.6362
],
[
"human melanoma",
12,
456,
38,
584,
0.5132
],
[
"human muscle",
9,
527,
58.56,
137,
0.2687
],
[
"human oesophagus",
47,
2777,
59.09,
2329,
0.6846
],
[
"human pancreas",
44,
1721,
39.11,
2436,
0.7473
],
[
"human peritoneum",
12,
294,
24.5,
511,
0.5636
],
[
"human placenta",
40,
1255,
31.38,
2415,
0.7855
],
[
"human pylorus",
12,
341,
28.42,
481,
0.744
],
[
"human rectum",
12,
411,
34.25,
390,
0.6034
],
[
"human salivory gland",
44,
2333,
53.02,
3577,
0.7204
],
[
"human spleen",
34,
1484,
43.65,
3876,
0.5416
],
[
"human testis",
12,
653,
54.42,
406,
0.5419
],
[
"human tongue",
40,
899,
22.48,
1628,
0.7473
],
[
"human tonsile",
12,
672,
56,
1166,
0.6115
],
[
"human umbilical cord",
11,
610,
55.45,
117,
0.0968
],
[
"mouse fat (white and brown)_subscapula",
42,
3355,
79.88,
557,
0.1084
],
[
"mouse femur",
6,
452,
75.33,
921,
0.6616
],
[
"mouse heart",
28,
2554,
91.21,
743,
0.2203
],
[
"mouse kidney",
40,
2917,
72.92,
1625,
0.4559
],
[
... (381 more characters in the session record)comparison Comparison runs for Smallest object to keep. The record keeps the scientist's choice.
Smallest object to count (pixels) n_objects Result 0 1894955 ok 15 149485 ok 30 66610 ok 60 35538 ok
decision card Smallest object to count (pixels)
Pixel groups with fewer pixels are removed before counting. Use 0 to keep all of them. Tiny groups are often noise and can change the count by a large factor. The model wants to run count_objects.
Suggested: 30 (The model proposed this value when it asked to run the step.)
Data that the model gave for this card
Smallest object to count (pixels) n_objects Result 0 1894955 ok 15 149485 ok 30 66610 ok 60 35538 ok n_objects depends on the choice: 1894955 with 0, 149485 with 15, 66610 with 30, 35538 with 60
Answer 30
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper states no size range. We remove objects under 30 pixels.
step n12 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-9/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-9/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (412 more characters in the session record)Model thinking, summary: I should compare the different threshold method options before reporting, so let me run that comparison now.
The model calls
compare_options.comparison run n13 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-10/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-10/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)comparison run n14 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 65132 objects; mean Dice 0.512 over 665 scored images
Outputs: per_group.csv (0795c84577e0), per_image.csv (88ac22ea3040).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | li |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 65132 objects; mean Dice 0.512 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 65132,
"total_objects": 65132,
"mean_objects_per_image": 97.94285714285714,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5118254135338345,
"mean_f1_iou_50": 0.1059387969924812,
"mean_average_f1": 0.04181624060150376
},
"outputs": [
{
"path": "{work}/count_objects-11/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-11/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
787,
65.58,
453,
0.5916
],
[
"human brain",
12,
746,
62.17,
150,
0.1103
],
[
"human cardia",
12,
547,
45.58,
767,
0.6625
],
[
"human cerebellum",
12,
948,
79,
653,
0.6205
],
[
"human epiglottis",
11,
669,
60.82,
261,
0.4571
],
[
"human jejunum",
10,
714,
71.4,
1033,
0.7355
],
[
"human kidney",
11,
442,
40.18,
1521,
0.8229
],
[
"human liver",
40,
7014,
175.35,
1471,
0.5512
],
[
"human lung",
11,
602,
54.73,
364,
0.5617
],
[
"human melanoma",
12,
710,
59.17,
584,
0.4275
],
[
"human muscle",
9,
1148,
127.56,
137,
0.2211
],
[
"human oesophagus",
47,
5814,
123.7,
2329,
0.6461
],
[
"human pancreas",
44,
2089,
47.48,
2436,
0.7209
],
[
"human peritoneum",
12,
533,
44.42,
511,
0.4927
],
[
"human placenta",
40,
2323,
58.08,
2415,
0.7878
],
[
"human pylorus",
12,
727,
60.58,
481,
0.6296
],
[
"human rectum",
12,
1065,
88.75,
390,
0.4665
],
[
"human salivory gland",
44,
3586,
81.5,
3577,
0.7177
],
[
"human spleen",
34,
2335,
68.68,
3876,
0.6939
],
[
"human testis",
12,
1341,
111.75,
406,
0.4466
],
[
"human tongue",
40,
2151,
53.77,
1628,
0.6604
],
[
"human tonsile",
12,
653,
54.42,
1166,
0.5766
],
[
"human umbilical cord",
11,
1224,
111.27,
117,
0.0719
],
[
"mouse fat (white and brown)_subscapula",
42,
3796,
90.38,
557,
0.0908
],
[
"mouse femur",
6,
731,
121.83,
921,
0.6908
],
[
"mouse heart",
28,
5149,
183.89,
743,
0.1688
],
[
"mouse kidney",
40,
6074,
151.85,
1625,
... (412 more characters in the session record)comparison run n15 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 16395 objects; mean Dice 0.355 over 665 scored images
Outputs: per_group.csv (e2995278e1af), per_image.csv (9d162c48af46).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | triangle |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 16395 objects; mean Dice 0.355 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 16395,
"total_objects": 16395,
"mean_objects_per_image": 24.654135338345863,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.3551672180451128,
"mean_f1_iou_50": 0.13387774436090225,
"mean_average_f1": 0.04810541353383459
},
"outputs": [
{
"path": "{work}/count_objects-12/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-12/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
252,
21,
453,
0.6816
],
[
"human brain",
12,
131,
10.92,
150,
0.5475
],
[
"human cardia",
12,
292,
24.33,
767,
0.5184
],
[
"human cerebellum",
12,
256,
21.33,
653,
0.73
],
[
"human epiglottis",
11,
144,
13.09,
261,
0.7523
],
[
"human jejunum",
10,
234,
23.4,
1033,
0.0939
],
[
"human kidney",
11,
392,
35.64,
1521,
0.1675
],
[
"human liver",
40,
1103,
27.57,
1471,
0.1507
],
[
"human lung",
11,
203,
18.45,
364,
0.6712
],
[
"human melanoma",
12,
345,
28.75,
584,
0.4204
],
[
"human muscle",
9,
168,
18.67,
137,
0.5278
],
[
"human oesophagus",
47,
1103,
23.47,
2329,
0.2734
],
[
"human pancreas",
44,
1314,
29.86,
2436,
0.3661
],
[
"human peritoneum",
12,
244,
20.33,
511,
0.2299
],
[
"human placenta",
40,
1255,
31.38,
2415,
0.6112
],
[
"human pylorus",
12,
364,
30.33,
481,
0.6706
],
[
"human rectum",
12,
283,
23.58,
390,
0.5956
],
[
"human salivory gland",
44,
2067,
46.98,
3577,
0.2667
],
[
"human spleen",
34,
978,
28.76,
3876,
0.1726
],
[
"human testis",
12,
347,
28.92,
406,
0.4555
],
[
"human tongue",
40,
892,
22.3,
1628,
0.5831
],
[
"human tonsile",
12,
212,
17.67,
1166,
0.0661
],
[
"human umbilical cord",
11,
156,
14.18,
117,
0.5915
],
[
"mouse fat (white and brown)_subscapula",
42,
455,
10.83,
557,
0.2438
],
[
"mouse femur",
6,
271,
45.17,
921,
0.2172
],
[
"mouse heart",
28,
519,
18.54,
743,
0.291
],
[
"mouse kidney",
40,
918,
22.95,
1625,
0.1546
],
[
... (386 more characters in the session record)comparison run n16 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66621 objects; mean Dice 0.531 over 665 scored images
Outputs: per_group.csv (24995c7336ff), per_image.csv (9b74743a65a9).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | isodata |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66621 objects; mean Dice 0.531 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66621,
"total_objects": 66621,
"mean_objects_per_image": 100.18195488721804,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5313412030075189,
"mean_f1_iou_50": 0.13456496240601504,
"mean_average_f1": 0.05140496240601504
},
"outputs": [
{
"path": "{work}/count_objects-13/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-13/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
399,
33.25,
453,
0.7038
],
[
"human brain",
12,
1056,
88,
150,
0.1256
],
[
"human cardia",
12,
476,
39.67,
767,
0.7199
],
[
"human cerebellum",
12,
981,
81.75,
653,
0.4136
],
[
"human epiglottis",
11,
560,
50.91,
261,
0.554
],
[
"human jejunum",
10,
673,
67.3,
1033,
0.7298
],
[
"human kidney",
11,
537,
48.82,
1521,
0.7872
],
[
"human liver",
40,
6216,
155.4,
1471,
0.5846
],
[
"human lung",
11,
375,
34.09,
364,
0.5232
],
[
"human melanoma",
12,
666,
55.5,
584,
0.4862
],
[
"human muscle",
9,
1218,
135.33,
137,
0.2516
],
[
"human oesophagus",
47,
6244,
132.85,
2329,
0.6615
],
[
"human pancreas",
44,
2196,
49.91,
2436,
0.7428
],
[
"human peritoneum",
12,
527,
43.92,
511,
0.546
],
[
"human placenta",
40,
2312,
57.8,
2415,
0.7807
],
[
"human pylorus",
12,
451,
37.58,
481,
0.717
],
[
"human rectum",
12,
921,
76.75,
390,
0.5529
],
[
"human salivory gland",
44,
3476,
79,
3577,
0.7189
],
[
"human spleen",
34,
2406,
70.76,
3876,
0.6822
],
[
"human testis",
12,
1303,
108.58,
406,
0.514
],
[
"human tongue",
40,
1592,
39.8,
1628,
0.7219
],
[
"human tonsile",
12,
852,
71,
1166,
0.6027
],
[
"human umbilical cord",
11,
1122,
102,
117,
0.0839
],
[
"mouse fat (white and brown)_subscapula",
42,
4892,
116.48,
557,
0.0964
],
[
"mouse femur",
6,
683,
113.83,
921,
0.6718
],
[
"mouse heart",
28,
5723,
204.39,
743,
0.1887
],
[
"mouse kidney",
40,
6316,
157.9,
1625,
0.4242
],
... (398 more characters in the session record)comparison run n17 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 16574 objects; mean Dice 0.305 over 665 scored images
Outputs: per_group.csv (0e55aec7f97d), per_image.csv (f56d00615975).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | yen |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 16574 objects; mean Dice 0.305 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 16574,
"total_objects": 16574,
"mean_objects_per_image": 24.923308270676692,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.30511323308270677,
"mean_f1_iou_50": 0.10875067669172933,
"mean_average_f1": 0.03850315789473684
},
"outputs": [
{
"path": "{work}/count_objects-14/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-14/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
202,
16.83,
453,
0.6347
],
[
"human brain",
12,
86,
7.17,
150,
0.4074
],
[
"human cardia",
12,
292,
24.33,
767,
0.5025
],
[
"human cerebellum",
12,
132,
11,
653,
0.3815
],
[
"human epiglottis",
11,
26,
2.36,
261,
0.2332
],
[
"human jejunum",
10,
487,
48.7,
1033,
0.3297
],
[
"human kidney",
11,
473,
43,
1521,
0.1903
],
[
"human liver",
40,
1032,
25.8,
1471,
0.1363
],
[
"human lung",
11,
121,
11,
364,
0.3486
],
[
"human melanoma",
12,
210,
17.5,
584,
0.2698
],
[
"human muscle",
9,
126,
14,
137,
0.4549
],
[
"human oesophagus",
47,
1433,
30.49,
2329,
0.3611
],
[
"human pancreas",
44,
1010,
22.95,
2436,
0.3309
],
[
"human peritoneum",
12,
333,
27.75,
511,
0.3832
],
[
"human placenta",
40,
1168,
29.2,
2415,
0.5556
],
[
"human pylorus",
12,
256,
21.33,
481,
0.5209
],
[
"human rectum",
12,
312,
26,
390,
0.6258
],
[
"human salivory gland",
44,
2215,
50.34,
3577,
0.3344
],
[
"human spleen",
34,
1163,
34.21,
3876,
0.1875
],
[
"human testis",
12,
498,
41.5,
406,
0.5581
],
[
"human tongue",
40,
841,
21.02,
1628,
0.5379
],
[
"human tonsile",
12,
580,
48.33,
1166,
0.2231
],
[
"human umbilical cord",
11,
68,
6.18,
117,
0.439
],
[
"mouse fat (white and brown)_subscapula",
42,
769,
18.31,
557,
0.0836
],
[
"mouse femur",
6,
361,
60.17,
921,
0.2441
],
[
"mouse heart",
28,
296,
10.57,
743,
0.1627
],
[
"mouse kidney",
40,
479,
11.97,
1625,
0.076
],
[
"mouse liver"
... (373 more characters in the session record)comparison Comparison runs for Threshold method. The record keeps the scientist's choice.
Threshold method n_objects Result otsu 66610 ok li 65132 ok triangle 16395 ok isodata 66621 ok yen 16574 ok
Result of compare_options: Compared 5 values of Threshold method as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, trut ...
Compared 5 values of Threshold method as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, truth_folder={data}/mahbod2024-nuinsseg/x, truth_match=label masks, min_object_size=30, stain_channel=hematoxylin, object_polarity=bright, pixel_size=0, exclude_border_objects=false, split_touching=false). Only that step was repeated. To compare another step, run it again first. The record still says "otsu".
Threshold method n_objects Result
otsu 66610 ok
li 65132 ok
triangle 16395 ok
isodata 66621 ok
yen 16574 ok
n_objects depends on the choice: 66610 with otsu, 65132 with li, 16395 with triangle, 66621 with isodata, 16574 with yenThe model calls
read_file.Result of read_file: Read 1345 of 1345 bytes from {work}/count_objects-9/per_group.csv.
group,n_images,total_objects,objects_per_image,total_truth,mean_dice human bladder,12,353,29.42,453,0.7325 human brain,12,1165,97.08,150,0.1323 human cardia,12,464,38.67,767,0.728 human cerebellum,12,399,33.25,653,0.5931 human epiglottis,11,353,32.09,261,0.6456 human jejunum,10,683,68.3,1033,0.7267 human kidney,11,559,50.82,1521,0.7792 human liver,40,5875,146.88,1471,0.593 human lung,11,301,27.36,364,0.6336 human melanoma,12,649,54.08,584,0.5075 human muscle,9,1228,136.44,137,0.2586 human oesophagus,47,6228,132.51,2329,0.6668 human pancreas,44,2232,50.73,2436,0.7458 human peritoneum,12,495,41.25,511,0.5582 human placenta,40,2275,56.88,2415,0.7803 human pylorus,12,427,35.58,481,0.7403 human rectum,12,819,68.25,390,0.589 human salivory gland,44,3427,77.89,3577,0.7175 human spleen,34,1959,57.62,3876,0.5405 human testis,12,1257,104.75,406,0.5273 human tongue,40,1324,33.1,1628,0.7426 human tonsile,12,921,76.75,1166,0.6075 human umbilical cord,11,1355,123.18,117,0.0917 mouse fat (white and brown)_subscapula,42,6405,152.5,557,0.1027 mouse femur,6,668,111.33,921,0.6648 mouse heart,28,5930,211.79,743,0.2015 mouse kidney,40,6327,158.18,1625,0.4368 mouse liver,36,7732,214.78,674,0.3735 mouse muscle_tibia,28,3530,126.07,172,0.103 mouse spleen,7,656,93.71,2029,0.7485 mouse thymus,6,614,102.33,1691,0.7075
Model thinking, summary: The comparison only gave me object counts, not Dice scores, and the count was way off (66610 vs 35138 true objects), so I should run count_objects for li, triangle, isodata, and yen separately to compute Dice for each.
The model runs run_script.
from ga_api import call, save_table
base="{data}/mahbod2024-nuinsseg/x"
rows=[]
for m in ["otsu","li","triangle","isodata","yen"]:
r=call("count_objects",path=base,pattern="*/tissue images/*.png",truth_folder=base,truth_match="label masks",threshold_method=m,min_object_size=30,stain_channel="hematoxylin",object_polarity="bright")
d=r.get("metrics",{})
rows.append({"method":m,"n_images":d.get("n_scored"),"found_objects":d.get("total_objects"),"truth_objects":d.get("total_truth"),"mean_dice":d.get("mean_dice"),"mean_f1_iou_50":d.get("mean_f1_iou_50"),"mean_average_f1":d.get("mean_average_f1")})
print(rows[-1])
save_table("threshold_method_comparison",rows)The harness lets the model use
run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.The model calls
count_objects (adapter scikit-image).step n18 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-15/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-15/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)The model calls
count_objects (adapter scikit-image).deviation The model asked for threshold_method = li. The scientist chose otsu for Threshold method. The harness kept otsu.
step n19 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-16/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-16/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)The model calls
count_objects (adapter scikit-image).deviation The model asked for threshold_method = triangle. The scientist chose otsu for Threshold method. The harness kept otsu.
step n20 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-17/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-17/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)The model calls
count_objects (adapter scikit-image).deviation The model asked for threshold_method = isodata. The scientist chose otsu for Threshold method. The harness kept otsu.
step n21 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-18/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-18/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)The model calls
count_objects (adapter scikit-image).deviation The model asked for threshold_method = yen. The scientist chose otsu for Threshold method. The harness kept otsu.
step n22 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| threshold_method | otsu |
| min_object_size | 30 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| pixel_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-19/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-19/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)step n23 run_script
The script ran in {work} and wrote 11 new file(s) to {work}.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32), per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32), per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32), per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32), per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32), threshold_method_comparison.csv (7c95cea554fb).
Arguments
| purpose | Score the other threshold methods against the hand-drawn masks |
| does | Runs the same nuclei count on all 665 patches with four more threshold methods. Saves Dice, F1 and object counts for each method in one table. |
| timeout_s | 1800 |
| code | from ga_api import call, save_table
base="{data}/mahbod2024-nuinsseg/x"
rows=[]
for m in ["otsu","li","triangle","isodata","yen"]:
r=call("count_objects",path=base,pattern="*/tissue images/*.png",truth_folder=base,truth_match="label masks",threshold_method=m,min_object_size=30,stain_channel="hematoxylin",object_polarity="bright")
d=r.get("metrics",{})
rows.append({"method":m,"n_images" ... |
Tool output
{'method': 'otsu', 'n_images': 665, 'found_objects': 66610, 'truth_objects': 35138, 'mean_dice': 0.5380503759398496, 'mean_f1_iou_50': 0.14801323308270675, 'mean_average_f1': 0.05780225563909775}
{'method': 'li', 'n_images': 665, 'found_objects': 66610, 'truth_objects': 35138, 'mean_dice': 0.5380503759398496, 'mean_f1_iou_50': 0.14801323308270675, 'mean_average_f1': 0.05780225563909775}
{'method': 'triangle', 'n_images': 665, 'found_objects': 66610, 'truth_objects': 35138, 'mean_dice': 0.5380503759398496, 'mean_f1_iou_50': 0.14801323308270675, 'mean_average_f1': 0.05780225563909775}
{'method': 'isodata', 'n_images': 665, 'found_objects': 66610, 'truth_objects': 35138, 'mean_dice': 0.5380503759398496, 'mean_f1_iou_50': 0.14801323308270675, 'mean_average_f1': 0.05780225563909775}
{'method': 'yen', 'n_images': 665, 'found_objects': 66610, 'truth_objects': 35138, 'mean_dice': 0.5380503759398496, 'mean_f1_iou_50': 0.14801323308270675, 'mean_average_f1': 0.05780225563909775}
Model Answer draft 1. The claim check and the review read it.
Outlines for all 665 patches, from 31 organ folders, are done. At the settings you chose, they overlap the hand-drawn masks only moderately. I did not get Dice scores for the other threshold methods, so I can't say yet whether a different method fits better.
What I did
- I read the folder. The 665 tissue images are 8-bit, 512 × 512 pixels. The 16-bit files are the label masks, so the mixed bit depth does not affect the segmentation.
- I ran the main segmentation with
count_objects, using scikit-image 0.26. The settings were: - Channel: hematoxylin.
- Polarity: bright.
- Threshold method: Otsu.
- Smallest object: 30 pixels.
- Border objects kept.
- No watershed split.
- Sizes in pixels, because the pixel size is 0.
- I scored every image against the files in the
label masksfolders. Each mask holds one number per nucleus. The "vague areas" masks were not used. - I compared the other threshold methods with
compare_options.
What I measured (main run: Otsu, 30 pixels)
- Images scored: 665. Each image is one replicate.
- Found objects: 66,610. Hand-drawn nuclei: 35,138. The outlines find about 1.9 times more objects than the hand-drawn masks.
- Mean Dice: 0.538. Dice measures pixel overlap. It is not an object count.
- Mean F1 at IoU 0.5: 0.148. IoU is intersection over union, the overlap between a found object and a true one.
- Mean F1 averaged over IoU 0.5 to 0.95: 0.058.
- Dice differs a lot between organs (
count_objects-9/per_group.csv): - Best: human kidney 0.779, human placenta 0.780, human bladder 0.733.
- Worst: human umbilical cord 0.092, mouse fat 0.103, mouse muscle_tibia 0.103, human brain 0.132.
- In the worst folders the tool finds many more objects than the truth. Human brain has 1,165 found against 150 true. Mouse heart has 5,930 found against 743 true.
Threshold method range (object counts only, compare_options)
| Method | Found objects |
|---|---|
| Otsu | 66,610 |
| Li | 65,132 |
| Isodata | 66,621 |
| Triangle | 16,395 |
| Yen | 16,574 |
- Otsu, Li and Isodata agree closely.
- Triangle and Yen find about four times fewer objects. They are far below the 35,138 true nuclei, so they probably merge or drop nuclei.
- The count therefore depends on the threshold method.
What is uncertain
- Dice by method is missing. I re-ran the folder for each method in a script. All five runs returned the same Otsu numbers, because the recorded method overrode my value. I discard that table,
threshold_method_comparison.csv. - A low F1 is expected here, because a plain threshold with no watershed merges touching nuclei and picks up non-nuclear stain. A mean Dice of 0.538 means the outlines only partly match the hand-drawn ones.
- The pooled numbers mix organs with very different results. Treat the per-organ table as the real result.
- No p-values apply. The objects in one image are not independent samples.
What waits for you
- Decide if I use
propose_changeto switch the recorded method to Li, Triangle or Yen. Then I can score each one against the masks. - Decide if I test watershed splitting (
split_touching), a larger minimum size, or hole filling. These changes aim to cut the extra objects. - Decide if I score the organs with a poor match separately.
claim check 40 numbers in the answer, 1 number without a source in the record.
The review found 4 problems. The model corrects the answer before the scientist sees it.
review The review sent the answer back to the model before the scientist saw it.
- The answer uses 30, 30, 0.5, 0.5, 0.058, 0.733, 65132, 66621, 16395, 16574 from a comparison run of another option (threshold_method), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison. - The answer names triangle, yen for "Threshold method", but the decision record says otsu. Report the value that was used. - 1 number in the answer match no logged tool result: 0.95. Run the tool that measures each number, or remove the number, or say how you derived it from logged results. - The answer says Dice scores for the other threshold methods are missing. The compare_options runs in the log did produce them: Li 0.512, Triangle 0.355, Isodata 0.531, Yen 0.305, against Otsu 0.538. The answer must report these values and say that Otsu scores best. Fix these problems. Then give the answer again.
Model thinking, summary: I don't actually see Dice values in the compare_options output I received, only n_objects, so I shouldn't report numbers I haven't verified. Let me check if per-run folders exist in the work directory for these compare runs to confirm.
The model calls
list_dir.Result of list_dir: 50 entries in {work}.
- ._count_objects-1 4096 - ._count_objects-10 4096 - ._count_objects-11 4096 - ._count_objects-12 4096 - ._count_objects-13 4096 - ._count_objects-14 4096 - ._count_objects-15 4096 - ._count_objects-16 4096 - ._count_objects-17 4096 - ._count_objects-18 4096 - ._count_objects-19 4096 - ._count_objects-2 4096 - ._count_objects-3 4096 - ._count_objects-4 4096 - ._count_objects-5 4096 - ._count_objects-6 4096 - ._count_objects-7 4096 - ._count_objects-8 4096 - ._count_objects-9 4096 - ._inspect_image-1 4096 - ._inspect_image-2 4096 - ._inspect_image-3 4096 - ._samples.csv 4096 - ._script-1 4096 - ._threshold_method_comparison.csv 4096 d count_objects-1 d count_objects-10 d count_objects-11 d count_objects-12 d count_objects-13 d count_objects-14 d count_objects-15 d count_objects-16 d count_objects-17 d count_objects-18 d count_objects-19 d count_objects-2 d count_objects-3 d count_objects-4 d count_objects-5 d count_objects-6 d count_objects-7 d count_objects-8 d count_objects-9 d inspect_image-1 d inspect_image-2 d inspect_image-3 - samples.csv 473879 d script-1 - threshold_method_comparison.csv 495
Model
Folders 10 to 14 probably hold the comparison runs. I compute Dice from their per-image tables.
The model runs run_script.
import pandas as pd
for i in range(9,15):
df=pd.read_csv(f"count_objects-{i}/per_image.csv")
cols=[c for c in df.columns if 'dice' in c.lower()]
print(i,len(df),list(df.columns)[:12])
n=[c for c in df.columns if 'object' in c.lower() or 'count' in c.lower()]
print({c:df[c].sum() for c in n if df[c].dtype!=object}, {c:df[c].mean() for c in cols})The harness lets the model use
run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.step n24 run_script
The script ran in {work} and wrote 0 new file(s) to {work}.
Arguments
| purpose | Read Dice from the five comparison runs |
| does | Opens the per-image tables of runs 9 to 14. Shows object totals and mean Dice for each run, so I can match runs to threshold methods. |
| code | import pandas as pd
for i in range(9,15):
df=pd.read_csv(f"count_objects-{i}/per_image.csv")
cols=[c for c in df.columns if 'dice' in c.lower()]
print(i,len(df),list(df.columns)[:12])
n=[c for c in df.columns if 'object' in c.lower() or 'count' in c.lower()]
print({c:df[c].sum() for c in n if df[c].dtype!=object}, {c:df[c].mean() for c in cols}) |
Tool output
9 665 ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
{'n_objects': np.int64(66610)} {'dice': np.float64(0.5380503759398496)}
10 665 ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
{'n_objects': np.int64(66610)} {'dice': np.float64(0.5380503759398496)}
11 665 ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
{'n_objects': np.int64(65132)} {'dice': np.float64(0.5118254135338345)}
12 665 ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
{'n_objects': np.int64(16395)} {'dice': np.float64(0.3551672180451128)}
13 665 ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
{'n_objects': np.int64(66621)} {'dice': np.float64(0.5313412030075189)}
14 665 ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
{'n_objects': np.int64(16574)} {'dice': np.float64(0.30511323308270677)}
Model thinking, summary: Just the Dice score is needed here, so I'll finalize the answer and rephrase the "0.5 to 0.95" threshold as averaging over several IoU levels rather than citing the raw range.
Model The model writes its answer. The claim check reads it. See the final answer.
claim check 37 numbers in the answer, 0 numbers without a source in the record.
claim check 37 numbers in the answer, 0 numbers without a source in the record.
Saved: results.xlsx (31 sheets). Folder: {work}
The final answer
The model wrote this answer. The harness shows it to the scientist after the review.
The outlines in all 665 patches (31 organ folders) overlap the hand-drawn masks only moderately. Otsu scored best of the five threshold methods, with a mean Dice of 0.538. My earlier statement that Dice for the other methods was missing was wrong. The comparison runs did record it. I have now read it from their per-image tables, in folders count_objects-10 to count_objects-14.
Settings used
- Tool and version:
count_objects, scikit-image 0.26. - Channel: hematoxylin, bright objects.
- Threshold method: Otsu, as the decision record says.
- Smallest object: 30 pixels.
- Border objects kept, no watershed split.
- Sizes in pixels, because the pixel size is 0.
- Truth: the 16-bit
label masksfiles, one number per nucleus. I did not use the "vague areas" masks. - Replicate: one image, 665 in total.
Main run (Otsu, count_objects-9)
- Found objects: 66,610. Hand-drawn nuclei: 35,138.
- Mean Dice: 0.538. Dice measures pixel overlap. It is not an object count.
- Mean F1 at IoU 0.5: 0.148. IoU is intersection over union, the overlap between a found object and a true one.
- Mean F1 averaged over several IoU levels: 0.058.
- Dice differs a lot between organs (
per_group.csv): - Best: human placenta 0.780, human kidney 0.779, human bladder 0.733.
- Worst: human umbilical cord 0.092, mouse fat 0.103, mouse muscle_tibia 0.103, human brain 0.132.
- In the worst folders the tool finds many more objects than the truth. Human brain has 1,165 found against 150 true.
Threshold method comparison (comparison runs, same settings otherwise)
| Method | Found objects | Mean Dice |
|---|---|---|
| Otsu (chosen) | 66,610 | 0.538 |
| Isodata | 66,621 | 0.531 |
| Li | 65,132 | 0.512 |
| Triangle | 16,395 | 0.355 |
| Yen | 16,574 | 0.305 |
- Otsu has the highest Dice. This holds for all five methods I tried.
- Otsu, Isodata and Li are close in both count and Dice.
- Triangle and Yen find about four times fewer objects. They also score much lower.
- Every method finds fewer than 35,138 true nuclei or far more than that. The count depends on the method.
What is uncertain
- The five-method run I made in a script returned Otsu numbers every time, because the recorded method overrode my value. I discard that table,
threshold_method_comparison.csv. The comparison table above comes from the comparison runs instead. - Low F1 is expected. A plain threshold with no watershed merges touching nuclei and picks up non-nuclear stain.
- The pooled numbers mix organs with very different results. Treat the per-organ table as the real result.
- No p-values apply, because objects in one image are not independent samples.
What waits for you
- Decide if I test watershed splitting (
split_touching), a larger minimum size, or hole filling to cut the extra objects. - Decide if I score the poorly matched organs separately.
Settings and step results that the answer does not mention. The harness adds them.
Step results that the answer does not mention, from the record:
- n3 inspect_image: 512 by 512 pixels, 1 channel(s), uint16, min 0, max 64
- n23 run_script: The script ran in {work} and wrote 11 new file(s) to {work}.
- n24 run_script: The script ran in {work} and wrote 0 new file(s) to {work}.
Settings used, from the decision record: Pixel size in micrometers (0 = uncalibrated): 0 · Image channel to segment: hematoxylin · Are the objects bright or dark in that channel?: bright · Threshold method: otsu · Smallest object to count (pixels): 30 · Remove objects that touch the image border: false · Split touching objects with a watershed: false.Checks
Review findings
The review recorded 9 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.
| Severity | From | Finding | Shown with the final answer |
|---|---|---|---|
| error | rulenumber_from_comparison | The answer uses 30, 0.5, 0.058, 0.733 from a comparison run of another option (threshold_method), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison. | yes |
| warning | ruleborder_objects_kept | Objects that touch the border are in the count. Report the count with and without them. | yes |
| info | ruletext_style | The answer breaks the text rules (ASD-STE100) in 1 place. Sentence 42 uses the passive voice: "is expected". Use the active voice. | yes |
| warning | referee model | The answer says Otsu is best for all five methods. This holds only for the pooled mean. Otsu leads Isodata by 0.007 Dice, and the log gives no spread. In some organs other methods score higher: Li on kidney (0.823 vs 0.779) and Triangle on epiglottis (0.752 vs 0.646). | yes |
| warning | referee model | The worst-organ Dice values (umbilical cord, mouse fat, mouse muscle_tibia) are not in any logged result. The tables show only the first 20 of 31 rows. The content of per_group.csv was read but is not logged, so these numbers cannot be checked. | yes |
| warning | referee model | The answer says it used scikit-image 0.26. No logged step shows the library version, so this claim has no source in the log. | yes |
| info | referee model | The method for each comparison run is assumed from the order of compare_options (otsu, li, triangle, isodata, yen). The log never confirms which method made which run. The script check in step 16 shows only counts and Dice per run folder. | yes |
| info | referee model | The report discloses that the in-script and direct calls with li, triangle, isodata and yen all fell back to Otsu, and it discards that table. This is correct. The five-method comparison rests only on the compare_options runs. | yes |
| info | referee model | The report shows the count at five threshold methods and says the count changes a lot (about four times fewer for Triangle and Yen). It states Dice is not an object count. It also states that the vague-area masks were not used, units are pixels, and no p-values were computed. These parts follow the standards. | yes |
Numbers in the answer
The last claim check read 37 numbers in the answer. 37 numbers match a logged result. 0 numbers have no source in the record.
Deviations
- The model asked for threshold_method = li. The scientist chose otsu for Threshold method. The harness kept otsu.
- The model asked for threshold_method = triangle. The scientist chose otsu for Threshold method. The harness kept otsu.
- The model asked for threshold_method = isodata. The scientist chose otsu for Threshold method. The harness kept otsu.
- The model asked for threshold_method = yen. The scientist chose otsu for Threshold method. The harness kept otsu.
Failed tool calls
No tool call failed.
Data integrity
Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.
| File | SHA-256 | Fetched data | Steps with this hash |
|---|---|---|---|
{data}/mahbod2024-nuinsseg/x128.0 KB | - | file not found or too large to hash | none |
A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.
How to repeat it
Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.
CUVETTE_DATA={data} bash bench/papers/mahbod2024-nuinsseg/fetch.shRun the same case with Cuvette. The script gives the same answers from bench/papers/mahbod2024-nuinsseg/bench.yaml.
cuvette bench papers --papers mahbod2024-nuinsseg --models claude:claude-sonnet-5-5
Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.
inspect_image(step n1)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x")The manual route gives the same numbers. An automatic test in Cuvette checks this.
inspect_image(step n2)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png")The manual route gives the same numbers. An automatic test in Cuvette checks this.
inspect_image(step n3)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif")The manual route gives the same numbers. An automatic test in Cuvette checks this.
count_objects(step n12)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
count_objects(step n18)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
count_objects(step n19)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
count_objects(step n20)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
count_objects(step n21)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
count_objects(step n22)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
run_script(step n23)Run the Python code in {work}/script-1/script.py
- Code only: this step has no route in the program menus. Run it with the script or flow export.
The program has no menu route for this step. To repeat it, run the code.
run_script(step n24)Run the Python code in {work}/script-2/script.py
- Code only: this step has no route in the program menus. Run it with the script or flow export.
The program has no menu route for this step. To repeat it, run the code.
Figure
Run facts
| Model | claude-sonnet-5-5 through the Anthropic service |
| Date | 2026-10-09 12:45:33 UTC |
| End of run | the model gave a final answer |
| Time | 798 s |
| Requests to the model | 10 |
| Tokensunits of text that the model read and wrote | 26 input, 5193 output, 119772 cache read, 33896 cache write |
| Cost estimate | $0.16 at list price, from the token counts |
| Tool calls | 16 (0 failed) |
| Adapters | scikit-image 0.1.1, program 0.26.0 |
| Session | 20261009-074533-aae6 |
Code hash of each step (24)
| Step | Tool | Program version | Code hash |
|---|---|---|---|
| n1 | inspect_image | 0.26.0 | cd246d8739be |
| n2 | inspect_image | 0.26.0 | cd246d8739be |
| n3 | inspect_image | 0.26.0 | cd246d8739be |
| n4 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n5 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n6 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n7 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n8 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n9 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n10 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n11 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n12 | count_objects | 0.26.0 | a909f01092a4 |
| n13 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n14 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n15 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n16 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n17 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n18 | count_objects | 0.26.0 | a909f01092a4 |
| n19 | count_objects | 0.26.0 | a909f01092a4 |
| n20 | count_objects | 0.26.0 | a909f01092a4 |
| n21 | count_objects | 0.26.0 | a909f01092a4 |
| n22 | count_objects | 0.26.0 | a909f01092a4 |
| n23 | run_script | - | 995d74a3af3a |
| n24 | run_script | - | 995d74a3af3a |
The code hash is a fingerprint of the adapter name, the adapter version, the tool and its definition in the adapter. If one of these changes, the hash changes.
Haiku · claude-haiku-5-5 · run 3 of 3 shown 3 of 3 values match, 1 of 1 correct in the final answer
The session
This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.
setup The decision record starts with these values. A script set them before the first message, from the answers that we wrote before the run.
- Image channel for the nuclei: hematoxylinWhere the answer comes from: Not in the paper. The paper trains its models on the RGB images. Hematoxylin stains the nuclei, so we use that channel.
- Are the objects bright or dark in the channel: brightWhere the answer comes from: Not in the paper. Nuclei have high values in the hematoxylin channel after color deconvolution.
- Remove objects that touch the image edge: falseWhere the answer comes from: Not in the paper. We keep the objects at the patch edge.
- Split touching nuclei: falseWhere the answer comes from: Not in the paper for a threshold method. Our baseline does no declumping.
- Pixel size: 0Where the answer comes from: Not in the paper text. The paper names a 40x objective with numerical aperture 0.75 but gives no micrometers per pixel.
- Unit of replication: images or fieldsWhere the answer comes from: Technical Validation section. The paper splits the patches into five folds of 133 images each.
harness note A note from the harness to the model. The scientist did not write it.
- Setup values: Design: - What is the unit of replication? (replication_unit): images or fields - Pixel size in micrometers (0 = uncalibrated) (pixel_size): 0 Segmentation: - Image channel to segment (stain_channel): hematoxylin - Are the objects bright or dark in that channel? (object_polarity): bright - Remove objects that touch the image border (exclude_border_objects): false - Split touching objects with a watershed (split_touching): false Ask the scientist: Threshold method (threshold_method), Smallest object to count (pixels) (min_object_size). The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist. - Mode is now watch: you run the steps.
Model thinking, summary: I should start by looking at the data folder structure and examining a sample image, since threshold_method and min_object_size are parameters I'll need to clarify with the scientist before proceeding.
Model
I will start by looking at the folder layout and one image, so I can check the data before I set up the count.
The model calls
inspect_data.Failed of inspect_data: Failed: The inspection did not run: /opt/homebrew/Cellar/python@3.12/3.12.10_1/Frameworks/Python.framework/Versions/3.12/Resources/Python.app/Contents/MacOS/Pyt ...
The model calls
list_dir.Result of list_dir: 31 entries in {data}/mahbod2024-nuinsseg/x.
d human bladder d human brain d human cardia d human cerebellum d human epiglottis d human jejunum d human kidney d human liver d human lung d human melanoma d human muscle d human oesophagus d human pancreas d human peritoneum d human placenta d human pylorus d human rectum d human salivory gland d human spleen d human testis d human tongue d human tonsile d human umbilical cord d mouse fat (white and brown)_subscapula d mouse femur d mouse heart d mouse kidney d mouse liver d mouse muscle_tibia d mouse spleen d mouse thymus
Model thinking, summary: I'm noting this dataset has 665 samples across 33 organs, with label masks stored as 16-bit tifs separate from the images—plus a "vague areas" set of 1330 samples that might live in additional folders. I should check the sample sheet and open one image to confirm the structure.
Model
The folder holds 33 organ folders and about 665 image and mask pairs. Next I will read the sample sheet and inspect one image.
The model calls
read_file.Result of read_file: Read 3000 of 473879 bytes from {work}/samples.csv.
sample,group,unit,batch,path
human_bladder_01,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif
human_bladder_02,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_02.tif
human_bladder_03,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_03.tif
human_bladder_04,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_04.tif
human_bladder_05,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_05.tif
human_bladder_06,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_06.tif
human_bladder_07,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_07.tif
human_bladder_08,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_08.tif
human_bladder_09,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_09.tif
human_bladder_10,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_10.tif
human_bladder_11,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_11.tif
human_bladder_12,label masks,,,{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_12.tif
human_bladder_01,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_01.png
human_bladder_02,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_02.png
human_bladder_03,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_03.png
human_bladder_04,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_04.png
human_bladder_05,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_05.png
human_bladder_06,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_06.png
human_bladder_07,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_07.png
human_bladder_08,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_08.png
human_bladder_09,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_09.png
human_bladder_10,mask binary,,,{data}/mahbod2024-nuinsseg/x/human bladder/mask binary/human_bladder_10.png
human_bladder_11,mask binary,,,{other volume}/guThe model calls
list_dir.Result of list_dir: 4 entries in {data}/mahbod2024-nuinsseg/x/human bladder.
d label masks d mask binary d tissue images d vague areas
Model
The label masks are 16-bit files, and the tissue images are PNG files. I will inspect one tissue image and its matching label mask.
The model calls
inspect_image (adapter scikit-image).step n1 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
512 by 512 pixels, 3 channel(s), uint8, min 14, max 174
Input file: {data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png SHA-256 bc59dd2d355a.
Arguments
| path | {data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png |
Tool output
{
"ok": true,
"summary": "512 by 512 pixels, 3 channel(s), uint8, min 14, max 174",
"metrics": {
"width": 512,
"height": 512,
"channels": 3,
"bit_depth": 8,
"min_value": 14,
"max_value": 174,
"mean_value": 106.69461186726888,
"saturated_fraction": 0
},
"data": {
"dtype": "uint8",
"warnings": [],
"calibrated": false
},
"outputs": []
}The model calls
inspect_image (adapter scikit-image).step n2 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
512 by 512 pixels, 1 channel(s), uint16, min 0, max 64
Input file: {data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif SHA-256 9ae5584ca3c5.
Arguments
| path | {data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif |
Tool output
{
"ok": true,
"summary": "512 by 512 pixels, 1 channel(s), uint16, min 0, max 64",
"metrics": {
"width": 512,
"height": 512,
"channels": 1,
"bit_depth": 16,
"min_value": 0,
"max_value": 64,
"mean_value": 1.8787879943847656,
"saturated_fraction": 0
},
"data": {
"dtype": "uint16",
"warnings": [],
"calibrated": false
},
"outputs": []
}Model
The label masks are 16-bit label images with up to 64 objects in this patch. Before I ask about the method, I will check which files have the 2344 x 1172 size, so the scoring pairs match.
The model runs run_script.
import os, glob, csv
from collections import Counter
from PIL import Image
root = "{data}/mahbod2024-nuinsseg/x"
rows = []
for organ in sorted(os.listdir(root)):
op = os.path.join(root, organ)
if not os.path.isdir(op):
continue
for sub in ["tissue images", "label masks", "mask binary", "vague areas"]:
sp = os.path.join(op, sub)
if not os.path.isdir(sp):
continue
for f in sorted(os.listdir(sp)):
if f.startswith('.'):
continue
p = os.path.join(sp, f)
try:
im = Image.open(p)
rows.append([organ, sub, f, im.size[0], im.size[1], im.mode])
except Exception as e:
rows.append([organ, sub, f, -1, -1, 'ERR ' + str(e)[:40]])
with open("size_inventory.csv", "w", newline="") as fh:
w = csv.writer(fh)
w.writerow(["organ", "folder", "file", "width", "height", "mode"])
w.writerows(rows)
c = Counter((r[1], r[3], r[4], r[5]) for r in rows)
for k, v in sorted(c.items()):
print(k, v)
print("organs:", len(set(r[0] for r in rows)))
The harness lets the model use
run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.step n3 run_script
The script ran in {work} and wrote 1 new file(s) to {work}.
Outputs: size_inventory.csv (f9d660a636b5).
Arguments
| purpose | Check the image size and bit depth of every tissue image and label mask, so the pairs can be matched. |
| does | Opens the header of each tissue image and each label mask in all organ folders, records the width, height and bit depth, and counts the sizes per folder type. Saves a table (size_inventory.csv). |
| code |
import os, glob, csv
from collections import Counter
from PIL import Image
root = "{data}/mahbod2024-nuinsseg/x"
rows = []
for organ in sorted(os.listdir(root)):
op = os.path.join(root, organ)
if not os.path.isdir(op):
continue
for sub in ["tissue images", "label masks", "mask binary", "vague areas"]:
sp = os.path.join(op, sub)
if not os.path.isdir(sp): ... |
Tool output
('label masks', 512, 512, 'I;16') 665
('mask binary', 512, 512, 'L') 665
('tissue images', 512, 512, 'RGB') 664
('tissue images', 512, 512, 'RGBA') 1
('vague areas', -1, -1, "ERR [Errno 21] Is a directory: '{other volume}/") 93
organs: 31
Model
The pairs match. All 665 tissue images and 665 label masks are 512 x 512 pixels. The label masks are 16-bit, so each object has its own number. I will set the plan, then ask about the two method choices that the harness reserves for you.
plan The model updates its plan.
- done Check file pairs and sizes across organ folders (done: 665 tissue images and 665 label masks, all 512 x 512).
- doing Ask the scientist for the threshold method and the smallest object size.
- todo Count nuclei per patch with the hematoxylin channel, bright objects, no border removal, no watershed.
- todo Run the other threshold methods and report the range of counts.
- todo Score the outlines against the 16-bit label masks with Dice, F1 and object counts, per organ and overall.
- todo Write the summary with n per organ and the unit of replication (patch).
The model calls
ask_scientist.decision card Which threshold method do you want for the nuclei outlines? Otsu is the usual...
Which threshold method do you want for the nuclei outlines? Otsu is the usual default for hematoxylin.
Options: otsu li triangle isodata yen mean minimum
Suggested: otsu as the main method. Then run the other methods and report the range of scores, because the method can change the count. (The threshold method sets which pixels count as nucleus. It changes the count and the overlap with the hand-drawn masks.)
Answer otsu as the main method. Then run the other methods and report the range of scores, because the method can change the count.
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper.
Result of ask_scientist: The scientist answered: otsu as the main method. Then run the other methods and report the range of scores, because the method can change the count. (answered f ...
Model thinking, summary: I should also ask about min_object_size and suggest a reasonable default value for it.
Model
The scientist chose Otsu as the main method. Next I will ask about the smallest object size.
The model calls
ask_scientist.decision card What is the smallest object to keep, in pixels? Smaller groups of pixels are ...
What is the smallest object to keep, in pixels? Smaller groups of pixels are removed as noise.
Options: 20 pixels 30 pixels 50 pixels other value
Suggested: 20 pixels. This removes single noise specks and keeps small nuclei. The value is a starting point. Check it on a few patches. (The minimum size removes small noise pixels. A value that is too high drops real small nuclei from the count.)
Answer 20 pixels. This removes single noise specks and keeps small nuclei. The value is a starting point. Check it on a few patches.
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper.
Result of ask_scientist: The scientist answered: 20 pixels. This removes single noise specks and keeps small nuclei. The value is a starting point. Check it on a few patches. (answered ...
Model
The scientist set the minimum object size to 20 pixels. Next I will run the count and the scoring on all tissue images, with Otsu, the hematoxylin channel, bright objects, and no border removal or watershed.
The model calls
count_objects (adapter scikit-image).paused The harness paused count_objects until the scientist chose: Threshold method, Smallest object to keep. The decision cards follow.
Comparing the options for "Threshold method" before it asks the scientist.
comparison run n4 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 1894955 objects; mean Dice 0.491 over 665 scored images
Outputs: per_group.csv (853bd10b304e), per_image.csv (9bccd8c00eb4).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 1894955 objects; mean Dice 0.491 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 1894955,
"total_objects": 1894955,
"mean_objects_per_image": 2849.5563909774437,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.4908461654135338,
"mean_f1_iou_50": 0.009448120300751879,
"mean_average_f1": 0.003823458646616541
},
"outputs": [
{
"path": "{work}/count_objects-1/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-1/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
27020,
2251.67,
453,
0.642
],
[
"human brain",
12,
33108,
2759,
150,
0.1121
],
[
"human cardia",
12,
20820,
1735,
767,
0.6844
],
[
"human cerebellum",
12,
6969,
580.75,
653,
0.5901
],
[
"human epiglottis",
11,
9933,
903,
261,
0.6153
],
[
"human jejunum",
10,
37063,
3706.3,
1033,
0.684
],
[
"human kidney",
11,
19265,
1751.36,
1521,
0.7819
],
[
"human liver",
40,
192526,
4813.15,
1471,
0.4819
],
[
"human lung",
11,
9098,
827.09,
364,
0.6118
],
[
"human melanoma",
12,
28400,
2366.67,
584,
0.4572
],
[
"human muscle",
9,
33221,
3691.22,
137,
0.2146
],
[
"human oesophagus",
47,
158641,
3375.34,
2329,
0.5954
],
[
"human pancreas",
44,
48638,
1105.41,
2436,
0.731
],
[
"human peritoneum",
12,
28391,
2365.92,
511,
0.5126
],
[
"human placenta",
40,
87267,
2181.68,
2415,
0.7457
],
[
"human pylorus",
12,
27721,
2310.08,
481,
0.6612
],
[
"human rectum",
12,
36398,
3033.17,
390,
0.4987
],
[
"human salivory gland",
44,
139296,
3165.82,
3577,
0.6761
],
[
"human spleen",
34,
55998,
1647,
3876,
0.5216
],
[
"human testis",
12,
42126,
3510.5,
406,
0.4487
],
[
"human tongue",
40,
77142,
1928.55,
1628,
0.6861
],
[
"human tonsile",
12,
25643,
2136.92,
1166,
0.5867
],
[
"human umbilical cord",
11,
43744,
3976.73,
117,
0.0698
],
[
"mouse fat (white and brown)_subscapula",
42,
103878,
2473.29,
557,
0.0863
],
[
"mouse femur",
6,
26960,
4493.33,
921,
0.6345
],
[
"mouse heart",
28,
106172,
3791.86,
743,
0.
... (504 more characters in the session record)comparison run n5 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 1902977 objects; mean Dice 0.463 over 665 scored images
Outputs: per_group.csv (c9c58464d3de), per_image.csv (2f8ae176a867).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | li |
| min_object_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 1902977 objects; mean Dice 0.463 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 1902977,
"total_objects": 1902977,
"mean_objects_per_image": 2861.6195488721805,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.46283774436090225,
"mean_f1_iou_50": 0.006028721804511279,
"mean_average_f1": 0.002441203007518797
},
"outputs": [
{
"path": "{work}/count_objects-2/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-2/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
44207,
3683.92,
453,
0.4735
],
[
"human brain",
12,
27083,
2256.92,
150,
0.0982
],
[
"human cardia",
12,
33933,
2827.75,
767,
0.5907
],
[
"human cerebellum",
12,
20249,
1687.42,
653,
0.6005
],
[
"human epiglottis",
11,
21610,
1964.55,
261,
0.4201
],
[
"human jejunum",
10,
36186,
3618.6,
1033,
0.6813
],
[
"human kidney",
11,
22810,
2073.64,
1521,
0.8101
],
[
"human liver",
40,
156081,
3902.03,
1471,
0.4576
],
[
"human lung",
11,
19577,
1779.73,
364,
0.5233
],
[
"human melanoma",
12,
33269,
2772.42,
584,
0.3831
],
[
"human muscle",
9,
29158,
3239.78,
137,
0.1881
],
[
"human oesophagus",
47,
148629,
3162.32,
2329,
0.5756
],
[
"human pancreas",
44,
59558,
1353.59,
2436,
0.6984
],
[
"human peritoneum",
12,
29395,
2449.58,
511,
0.4561
],
[
"human placenta",
40,
110605,
2765.12,
2415,
0.7334
],
[
"human pylorus",
12,
47648,
3970.67,
481,
0.5079
],
[
"human rectum",
12,
37697,
3141.42,
390,
0.4015
],
[
"human salivory gland",
44,
157550,
3580.68,
3577,
0.6595
],
[
"human spleen",
34,
74901,
2202.97,
3876,
0.6595
],
[
"human testis",
12,
40114,
3342.83,
406,
0.3846
],
[
"human tongue",
40,
123550,
3088.75,
1628,
0.5773
],
[
"human tonsile",
12,
22544,
1878.67,
1166,
0.56
],
[
"human umbilical cord",
11,
38989,
3544.45,
117,
0.0581
],
[
"mouse fat (white and brown)_subscapula",
42,
80107,
1907.31,
557,
0.0802
],
[
"mouse femur",
6,
28469,
4744.83,
921,
0.634
],
[
"mouse heart",
28,
77945,
2783
... (524 more characters in the session record)comparison run n6 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 1891610 objects; mean Dice 0.485 over 665 scored images
Outputs: per_group.csv (e332e4808dbc), per_image.csv (9d52adf9c7be).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | isodata |
| min_object_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 1891610 objects; mean Dice 0.485 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 1891610,
"total_objects": 1891610,
"mean_objects_per_image": 2844.5263157894738,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.48485353383458646,
"mean_f1_iou_50": 0.008556992481203007,
"mean_average_f1": 0.0033469172932330824
},
"outputs": [
{
"path": "{work}/count_objects-3/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-3/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
30953,
2579.42,
453,
0.6055
],
[
"human brain",
12,
31209,
2600.75,
150,
0.1081
],
[
"human cardia",
12,
22368,
1864,
767,
0.674
],
[
"human cerebellum",
12,
21197,
1766.42,
653,
0.4059
],
[
"human epiglottis",
11,
15490,
1408.18,
261,
0.5272
],
[
"human jejunum",
10,
36985,
3698.5,
1033,
0.6839
],
[
"human kidney",
11,
19477,
1770.64,
1521,
0.7874
],
[
"human liver",
40,
184734,
4618.35,
1471,
0.476
],
[
"human lung",
11,
12064,
1096.73,
364,
0.5021
],
[
"human melanoma",
12,
29470,
2455.83,
584,
0.4375
],
[
"human muscle",
9,
32003,
3555.89,
137,
0.2105
],
[
"human oesophagus",
47,
153020,
3255.74,
2329,
0.5925
],
[
"human pancreas",
44,
50490,
1147.5,
2436,
0.7266
],
[
"human peritoneum",
12,
29086,
2423.83,
511,
0.501
],
[
"human placenta",
40,
88119,
2202.97,
2415,
0.7452
],
[
"human pylorus",
12,
31126,
2593.83,
481,
0.6312
],
[
"human rectum",
12,
37716,
3143,
390,
0.4702
],
[
"human salivory gland",
44,
143005,
3250.11,
3577,
0.6741
],
[
"human spleen",
34,
66112,
1944.47,
3876,
0.6567
],
[
"human testis",
12,
41867,
3488.92,
406,
0.4386
],
[
"human tongue",
40,
86933,
2173.32,
1628,
0.6636
],
[
"human tonsile",
12,
25208,
2100.67,
1166,
0.5825
],
[
"human umbilical cord",
11,
38272,
3479.27,
117,
0.0653
],
[
"mouse fat (white and brown)_subscapula",
42,
90819,
2162.36,
557,
0.0833
],
[
"mouse femur",
6,
27561,
4593.5,
921,
0.6341
],
[
"mouse heart",
28,
96157,
3434.18,
... (516 more characters in the session record)comparison run n7 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 620691 objects; mean Dice 0.387 over 665 scored images
Outputs: per_group.csv (9f42adba2e98), per_image.csv (27b89957f986).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | triangle |
| min_object_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 620691 objects; mean Dice 0.387 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 620691,
"total_objects": 620691,
"mean_objects_per_image": 933.36992481203,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.3868984962406015,
"mean_f1_iou_50": 0.009778646616541351,
"mean_average_f1": 0.0035595488721804507
},
"outputs": [
{
"path": "{work}/count_objects-4/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-4/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
8166,
680.5,
453,
0.6689
],
[
"human brain",
12,
9979,
831.58,
150,
0.5013
],
[
"human cardia",
12,
8445,
703.75,
767,
0.5234
],
[
"human cerebellum",
12,
5223,
435.25,
653,
0.7214
],
[
"human epiglottis",
11,
3540,
321.82,
261,
0.7002
],
[
"human jejunum",
10,
10113,
1011.3,
1033,
0.1601
],
[
"human kidney",
11,
9940,
903.64,
1521,
0.2134
],
[
"human liver",
40,
52161,
1304.03,
1471,
0.2584
],
[
"human lung",
11,
4540,
412.73,
364,
0.6691
],
[
"human melanoma",
12,
8978,
748.17,
584,
0.4307
],
[
"human muscle",
9,
9558,
1062,
137,
0.4741
],
[
"human oesophagus",
47,
53806,
1144.81,
2329,
0.3458
],
[
"human pancreas",
44,
21176,
481.27,
2436,
0.388
],
[
"human peritoneum",
12,
9736,
811.33,
511,
0.2917
],
[
"human placenta",
40,
41631,
1040.78,
2415,
0.635
],
[
"human pylorus",
12,
8887,
740.58,
481,
0.6739
],
[
"human rectum",
12,
10360,
863.33,
390,
0.6017
],
[
"human salivory gland",
44,
48251,
1096.61,
3577,
0.3354
],
[
"human spleen",
34,
23066,
678.41,
3876,
0.2216
],
[
"human testis",
12,
12397,
1033.08,
406,
0.4861
],
[
"human tongue",
40,
27942,
698.55,
1628,
0.5987
],
[
"human tonsile",
12,
10010,
834.17,
1166,
0.1283
],
[
"human umbilical cord",
11,
9771,
888.27,
117,
0.4629
],
[
"mouse fat (white and brown)_subscapula",
42,
43225,
1029.17,
557,
0.2551
],
[
"mouse femur",
6,
9216,
1536,
921,
0.2877
],
[
"mouse heart",
28,
29892,
1067.57,
743,
0.3313
],
[
"mouse
... (475 more characters in the session record)comparison Comparison runs for Threshold method. The record keeps the scientist's choice.
Threshold method n_objects Result otsu 1894955 ok li 1902977 ok isodata 1891610 ok triangle 620691 ok
decision card Threshold method
The threshold decides which pixels are objects. Different methods can give different counts. The model runs the main methods and shows you the counts before you choose. The model wants to run count_objects.
Options: otsu li triangle isodata yen mean minimum
Suggested: otsu (The model proposed this value when it asked to run the step.)
Data that the model gave for this card
Threshold method n_objects Result otsu 1894955 ok li 1902977 ok isodata 1891610 ok triangle 620691 ok n_objects depends on the choice: 1894955 with otsu, 1902977 with li, 1891610 with isodata, 620691 with triangle
Answer otsu
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper uses deep learning. We use a global Otsu threshold as a simple baseline.
Comparing the options for "Smallest object to count (pixels)" before it asks the scientist.
comparison run n8 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 1894955 objects; mean Dice 0.491 over 665 scored images
Outputs: per_group.csv (853bd10b304e), per_image.csv (9bccd8c00eb4).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 0 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 1894955 objects; mean Dice 0.491 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 1894955,
"total_objects": 1894955,
"mean_objects_per_image": 2849.5563909774437,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.4908461654135338,
"mean_f1_iou_50": 0.009448120300751879,
"mean_average_f1": 0.003823458646616541
},
"outputs": [
{
"path": "{work}/count_objects-5/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-5/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
27020,
2251.67,
453,
0.642
],
[
"human brain",
12,
33108,
2759,
150,
0.1121
],
[
"human cardia",
12,
20820,
1735,
767,
0.6844
],
[
"human cerebellum",
12,
6969,
580.75,
653,
0.5901
],
[
"human epiglottis",
11,
9933,
903,
261,
0.6153
],
[
"human jejunum",
10,
37063,
3706.3,
1033,
0.684
],
[
"human kidney",
11,
19265,
1751.36,
1521,
0.7819
],
[
"human liver",
40,
192526,
4813.15,
1471,
0.4819
],
[
"human lung",
11,
9098,
827.09,
364,
0.6118
],
[
"human melanoma",
12,
28400,
2366.67,
584,
0.4572
],
[
"human muscle",
9,
33221,
3691.22,
137,
0.2146
],
[
"human oesophagus",
47,
158641,
3375.34,
2329,
0.5954
],
[
"human pancreas",
44,
48638,
1105.41,
2436,
0.731
],
[
"human peritoneum",
12,
28391,
2365.92,
511,
0.5126
],
[
"human placenta",
40,
87267,
2181.68,
2415,
0.7457
],
[
"human pylorus",
12,
27721,
2310.08,
481,
0.6612
],
[
"human rectum",
12,
36398,
3033.17,
390,
0.4987
],
[
"human salivory gland",
44,
139296,
3165.82,
3577,
0.6761
],
[
"human spleen",
34,
55998,
1647,
3876,
0.5216
],
[
"human testis",
12,
42126,
3510.5,
406,
0.4487
],
[
"human tongue",
40,
77142,
1928.55,
1628,
0.6861
],
[
"human tonsile",
12,
25643,
2136.92,
1166,
0.5867
],
[
"human umbilical cord",
11,
43744,
3976.73,
117,
0.0698
],
[
"mouse fat (white and brown)_subscapula",
42,
103878,
2473.29,
557,
0.0863
],
[
"mouse femur",
6,
26960,
4493.33,
921,
0.6345
],
[
"mouse heart",
28,
106172,
3791.86,
743,
0.
... (504 more characters in the session record)comparison run n9 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 149485 objects; mean Dice 0.527 over 665 scored images
Outputs: per_group.csv (a37a55ad5861), per_image.csv (ba0b17ed4556).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 15 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 149485 objects; mean Dice 0.527 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 149485,
"total_objects": 149485,
"mean_objects_per_image": 224.78947368421052,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5273685714285714,
"mean_f1_iou_50": 0.1104572932330827,
"mean_average_f1": 0.04424015037593985
},
"outputs": [
{
"path": "{work}/count_objects-6/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-6/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
704,
58.67,
453,
0.7236
],
[
"human brain",
12,
2629,
219.08,
150,
0.1266
],
[
"human cardia",
12,
795,
66.25,
767,
0.7234
],
[
"human cerebellum",
12,
649,
54.08,
653,
0.5934
],
[
"human epiglottis",
11,
616,
56,
261,
0.6418
],
[
"human jejunum",
10,
1668,
166.8,
1033,
0.7215
],
[
"human kidney",
11,
854,
77.64,
1521,
0.7816
],
[
"human liver",
40,
16936,
423.4,
1471,
0.5625
],
[
"human lung",
11,
480,
43.64,
364,
0.6304
],
[
"human melanoma",
12,
1257,
104.75,
584,
0.4988
],
[
"human muscle",
9,
2985,
331.67,
137,
0.2474
],
[
"human oesophagus",
47,
14944,
317.96,
2329,
0.6468
],
[
"human pancreas",
44,
3321,
75.48,
2436,
0.7433
],
[
"human peritoneum",
12,
1244,
103.67,
511,
0.5502
],
[
"human placenta",
40,
4884,
122.1,
2415,
0.7745
],
[
"human pylorus",
12,
715,
59.58,
481,
0.734
],
[
"human rectum",
12,
2137,
178.08,
390,
0.57
],
[
"human salivory gland",
44,
6487,
147.43,
3577,
0.7124
],
[
"human spleen",
34,
3141,
92.38,
3876,
0.5381
],
[
"human testis",
12,
2841,
236.75,
406,
0.5099
],
[
"human tongue",
40,
2886,
72.15,
1628,
0.7342
],
[
"human tonsile",
12,
1484,
123.67,
1166,
0.6036
],
[
"human umbilical cord",
11,
3366,
306,
117,
0.0859
],
[
"mouse fat (white and brown)_subscapula",
42,
12811,
305.02,
557,
0.0974
],
[
"mouse femur",
6,
1310,
218.33,
921,
0.6625
],
[
"mouse heart",
28,
14681,
524.32,
743,
0.1822
],
[
"mouse kidney",
40,
16094,
402.35
... (433 more characters in the session record)comparison run n10 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-7/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-7/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (412 more characters in the session record)comparison run n11 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 35538 objects; mean Dice 0.547 over 665 scored images
Outputs: per_group.csv (eadb61857fce), per_image.csv (9ae6cd367f00).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 60 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 35538 objects; mean Dice 0.547 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 35538,
"total_objects": 35538,
"mean_objects_per_image": 53.440601503759396,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5465819548872181,
"mean_f1_iou_50": 0.1782366917293233,
"mean_average_f1": 0.06803609022556391
},
"outputs": [
{
"path": "{work}/count_objects-8/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-8/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
266,
22.17,
453,
0.7364
],
[
"human brain",
12,
642,
53.5,
150,
0.1373
],
[
"human cardia",
12,
372,
31,
767,
0.7307
],
[
"human cerebellum",
12,
271,
22.58,
653,
0.5928
],
[
"human epiglottis",
11,
221,
20.09,
261,
0.6497
],
[
"human jejunum",
10,
396,
39.6,
1033,
0.7288
],
[
"human kidney",
11,
453,
41.18,
1521,
0.7765
],
[
"human liver",
40,
2343,
58.58,
1471,
0.6144
],
[
"human lung",
11,
229,
20.82,
364,
0.6362
],
[
"human melanoma",
12,
456,
38,
584,
0.5132
],
[
"human muscle",
9,
527,
58.56,
137,
0.2687
],
[
"human oesophagus",
47,
2777,
59.09,
2329,
0.6846
],
[
"human pancreas",
44,
1721,
39.11,
2436,
0.7473
],
[
"human peritoneum",
12,
294,
24.5,
511,
0.5636
],
[
"human placenta",
40,
1255,
31.38,
2415,
0.7855
],
[
"human pylorus",
12,
341,
28.42,
481,
0.744
],
[
"human rectum",
12,
411,
34.25,
390,
0.6034
],
[
"human salivory gland",
44,
2333,
53.02,
3577,
0.7204
],
[
"human spleen",
34,
1484,
43.65,
3876,
0.5416
],
[
"human testis",
12,
653,
54.42,
406,
0.5419
],
[
"human tongue",
40,
899,
22.48,
1628,
0.7473
],
[
"human tonsile",
12,
672,
56,
1166,
0.6115
],
[
"human umbilical cord",
11,
610,
55.45,
117,
0.0968
],
[
"mouse fat (white and brown)_subscapula",
42,
3355,
79.88,
557,
0.1084
],
[
"mouse femur",
6,
452,
75.33,
921,
0.6616
],
[
"mouse heart",
28,
2554,
91.21,
743,
0.2203
],
[
"mouse kidney",
40,
2917,
72.92,
1625,
0.4559
],
[
... (381 more characters in the session record)comparison Comparison runs for Smallest object to keep. The record keeps the scientist's choice.
Smallest object to count (pixels) n_objects Result 0 1894955 ok 15 149485 ok 30 66610 ok 60 35538 ok
decision card Smallest object to count (pixels)
Pixel groups with fewer pixels are removed before counting. Use 0 to keep all of them. Tiny groups are often noise and can change the count by a large factor. The model wants to run count_objects.
Suggested: 20 (The model proposed this value when it asked to run the step.)
Data that the model gave for this card
Smallest object to count (pixels) n_objects Result 0 1894955 ok 15 149485 ok 30 66610 ok 60 35538 ok n_objects depends on the choice: 1894955 with 0, 149485 with 15, 66610 with 30, 35538 with 60
Answer 30
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper states no size range. We remove objects under 30 pixels.
step n12 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-9/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-9/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (412 more characters in the session record)Model
The scientist's answer of otsu matches my call. The output's manual route, however, lists a minimum object size of 30 pixels, not the 20 pixels that the scientist chose. I will record this and check which value the run used.
note Deviation check: I passed min_object_size 20 (scientist answer). The count_objects manual route lists min_object_size 30. The run used one of these values. I will rerun the count with 20 and 30 to see which value the record holds, before I report any count.
The model calls
compare_options.comparison run n13 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 106008 objects; mean Dice 0.532 over 665 scored images
Outputs: per_group.csv (4427856de093), per_image.csv (328d1eca2744).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 20 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 106008 objects; mean Dice 0.532 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 106008,
"total_objects": 106008,
"mean_objects_per_image": 159.41052631578947,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5319651127819549,
"mean_f1_iou_50": 0.12667849624060148,
"mean_average_f1": 0.05019804511278196
},
"outputs": [
{
"path": "{work}/count_objects-10/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-10/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
504,
42,
453,
0.728
],
[
"human brain",
12,
1875,
156.25,
150,
0.129
],
[
"human cardia",
12,
609,
50.75,
767,
0.7257
],
[
"human cerebellum",
12,
518,
43.17,
653,
0.5933
],
[
"human epiglottis",
11,
484,
44,
261,
0.6432
],
[
"human jejunum",
10,
1104,
110.4,
1033,
0.7246
],
[
"human kidney",
11,
684,
62.18,
1521,
0.7806
],
[
"human liver",
40,
11069,
276.73,
1471,
0.5759
],
[
"human lung",
11,
387,
35.18,
364,
0.6316
],
[
"human melanoma",
12,
914,
76.17,
584,
0.503
],
[
"human muscle",
9,
2085,
231.67,
137,
0.2521
],
[
"human oesophagus",
47,
10504,
223.49,
2329,
0.655
],
[
"human pancreas",
44,
2772,
63,
2436,
0.7443
],
[
"human peritoneum",
12,
829,
69.08,
511,
0.5538
],
[
"human placenta",
40,
3518,
87.95,
2415,
0.7769
],
[
"human pylorus",
12,
555,
46.25,
481,
0.7369
],
[
"human rectum",
12,
1386,
115.5,
390,
0.5787
],
[
"human salivory gland",
44,
4782,
108.68,
3577,
0.7149
],
[
"human spleen",
34,
2505,
73.68,
3876,
0.5393
],
[
"human testis",
12,
2010,
167.5,
406,
0.5176
],
[
"human tongue",
40,
2007,
50.17,
1628,
0.7381
],
[
"human tonsile",
12,
1202,
100.17,
1166,
0.6053
],
[
"human umbilical cord",
11,
2304,
209.45,
117,
0.0883
],
[
"mouse fat (white and brown)_subscapula",
42,
9612,
228.86,
557,
0.0995
],
[
"mouse femur",
6,
964,
160.67,
921,
0.6642
],
[
"mouse heart",
28,
10226,
365.21,
743,
0.1899
],
[
"mouse kidney",
40,
10859,
271.48,
16
... (419 more characters in the session record)comparison run n14 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-11/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-11/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)comparison Comparison runs for Smallest object to keep. The record keeps the scientist's choice.
Smallest object to count (pixels) n_objects Result 20 106008 ok 30 66610 ok
Result of compare_options: Compared 2 values of Smallest object to count (pixels) as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue i ...
Compared 2 values of Smallest object to count (pixels) as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, stain_channel=hematoxylin, object_polarity=bright, threshold_method=otsu, exclude_border_objects=false, split_touching=false, truth_folder={data}/mahbod2024-nuinsseg/x, truth_match=label masks, pixel_size=0). Only that step was repeated. To compare another step, run it again first. The record still says 30.
Smallest object to count (pixels) n_objects Result
20 106008 ok
30 66610 ok
n_objects depends on the choice: 106008 with 20, 66610 with 30Model thinking, summary: There's still a discrepancy—the record shows 30, but the scientist agreed to 20. I shouldn't silently use 30; I need to call propose_change with key min_object_size and value 20, citing the scientist's agreement, then rerun the count once accepted.
Model
The record still holds 30 pixels. The scientist chose 20 pixels, so I will ask to change the record to 20.
The model calls
propose_change.decision card Change Smallest object to count (pixels)?
The model asks to change "Smallest object to count (pixels)" from 30 to 20. Reason: The scientist answered 20 pixels. The record holds 30, so the first count used a value the scientist did not choose. The count with 20 pixels gives 106008 objects, against 66610 with 30.
Options: Change to 20 Keep 30
Suggested: 20 (The scientist answered 20 pixels. The record holds 30, so the first count used a value the scientist did not choose. The count with 20 pixels gives 106008 objects, against 66610 with 30.)
Answer 30
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper states no size range. We remove objects under 30 pixels.
Result of propose_change: The scientist answered. The record now says 30 for Smallest object to count (pixels).
Model thinking, summary: The record still shows 30 even after the scientist's reply, which is ambiguous — it's unclear if they rejected the change to 20 or something else happened. I should just ask the scientist directly which value they actually want.
Model
The record still says 30 after the proposal. I will ask the scientist directly which value to use.
The model calls
ask_scientist.decision card The record holds 30 pixels for the smallest object size, but you answered 20 ...
The record holds 30 pixels for the smallest object size, but you answered 20 pixels. Which value do you want for the final count: 20 or 30?
Options: 20 pixels (as answered) 30 pixels (as recorded)
Suggested: 20 pixels, as you answered. It keeps small nuclei. At 20 pixels the count is 106008 objects, against 66610 at 30 pixels. Both runs are already done. (The two values give very different object counts and different scores. The final numbers must use the value you chose.)
Answer 20 pixels, as you answered. It keeps small nuclei. At 20 pixels the count is 106008 objects, against 66610 at 30 pixels. Both runs are already done.
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper.
Result of ask_scientist: The scientist answered: 20 pixels, as you answered. It keeps small nuclei. At 20 pixels the count is 106008 objects, against 66610 at 30 pixels. Both runs are a ...
Model thinking, summary: The scientist confirms 20, but the record still shows 30, so I need to rerun with the correct threshold of 20 to get a proper final comparison — the earlier run at 20 only gave object count without the scoring output I actually need.
Model
The scientist confirmed 20 pixels. I will rerun the full count and score with 20 pixels, and check the method in the output.
The model calls
count_objects (adapter scikit-image).deviation The model asked for min_object_size = 20. The scientist chose 30 for Smallest object to keep. The harness kept 30.
step n15 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-12/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-12/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)Model
The harness again used 30 pixels for the scored run, even though the scientist chose 20. I cannot get a scored 20-pixel result until the record changes. I will run the other threshold methods now, labelled as the 30-pixel record, so the range is ready.
The model calls
compare_options.comparison run n16 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 65132 objects; mean Dice 0.512 over 665 scored images
Outputs: per_group.csv (0795c84577e0), per_image.csv (88ac22ea3040).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | li |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 65132 objects; mean Dice 0.512 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 65132,
"total_objects": 65132,
"mean_objects_per_image": 97.94285714285714,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5118254135338345,
"mean_f1_iou_50": 0.1059387969924812,
"mean_average_f1": 0.04181624060150376
},
"outputs": [
{
"path": "{work}/count_objects-13/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-13/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
787,
65.58,
453,
0.5916
],
[
"human brain",
12,
746,
62.17,
150,
0.1103
],
[
"human cardia",
12,
547,
45.58,
767,
0.6625
],
[
"human cerebellum",
12,
948,
79,
653,
0.6205
],
[
"human epiglottis",
11,
669,
60.82,
261,
0.4571
],
[
"human jejunum",
10,
714,
71.4,
1033,
0.7355
],
[
"human kidney",
11,
442,
40.18,
1521,
0.8229
],
[
"human liver",
40,
7014,
175.35,
1471,
0.5512
],
[
"human lung",
11,
602,
54.73,
364,
0.5617
],
[
"human melanoma",
12,
710,
59.17,
584,
0.4275
],
[
"human muscle",
9,
1148,
127.56,
137,
0.2211
],
[
"human oesophagus",
47,
5814,
123.7,
2329,
0.6461
],
[
"human pancreas",
44,
2089,
47.48,
2436,
0.7209
],
[
"human peritoneum",
12,
533,
44.42,
511,
0.4927
],
[
"human placenta",
40,
2323,
58.08,
2415,
0.7878
],
[
"human pylorus",
12,
727,
60.58,
481,
0.6296
],
[
"human rectum",
12,
1065,
88.75,
390,
0.4665
],
[
"human salivory gland",
44,
3586,
81.5,
3577,
0.7177
],
[
"human spleen",
34,
2335,
68.68,
3876,
0.6939
],
[
"human testis",
12,
1341,
111.75,
406,
0.4466
],
[
"human tongue",
40,
2151,
53.77,
1628,
0.6604
],
[
"human tonsile",
12,
653,
54.42,
1166,
0.5766
],
[
"human umbilical cord",
11,
1224,
111.27,
117,
0.0719
],
[
"mouse fat (white and brown)_subscapula",
42,
3796,
90.38,
557,
0.0908
],
[
"mouse femur",
6,
731,
121.83,
921,
0.6908
],
[
"mouse heart",
28,
5149,
183.89,
743,
0.1688
],
[
"mouse kidney",
40,
6074,
151.85,
1625,
... (412 more characters in the session record)comparison run n17 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 16395 objects; mean Dice 0.355 over 665 scored images
Outputs: per_group.csv (e2995278e1af), per_image.csv (9d162c48af46).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | triangle |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 16395 objects; mean Dice 0.355 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 16395,
"total_objects": 16395,
"mean_objects_per_image": 24.654135338345863,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.3551672180451128,
"mean_f1_iou_50": 0.13387774436090225,
"mean_average_f1": 0.04810541353383459
},
"outputs": [
{
"path": "{work}/count_objects-14/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-14/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
252,
21,
453,
0.6816
],
[
"human brain",
12,
131,
10.92,
150,
0.5475
],
[
"human cardia",
12,
292,
24.33,
767,
0.5184
],
[
"human cerebellum",
12,
256,
21.33,
653,
0.73
],
[
"human epiglottis",
11,
144,
13.09,
261,
0.7523
],
[
"human jejunum",
10,
234,
23.4,
1033,
0.0939
],
[
"human kidney",
11,
392,
35.64,
1521,
0.1675
],
[
"human liver",
40,
1103,
27.57,
1471,
0.1507
],
[
"human lung",
11,
203,
18.45,
364,
0.6712
],
[
"human melanoma",
12,
345,
28.75,
584,
0.4204
],
[
"human muscle",
9,
168,
18.67,
137,
0.5278
],
[
"human oesophagus",
47,
1103,
23.47,
2329,
0.2734
],
[
"human pancreas",
44,
1314,
29.86,
2436,
0.3661
],
[
"human peritoneum",
12,
244,
20.33,
511,
0.2299
],
[
"human placenta",
40,
1255,
31.38,
2415,
0.6112
],
[
"human pylorus",
12,
364,
30.33,
481,
0.6706
],
[
"human rectum",
12,
283,
23.58,
390,
0.5956
],
[
"human salivory gland",
44,
2067,
46.98,
3577,
0.2667
],
[
"human spleen",
34,
978,
28.76,
3876,
0.1726
],
[
"human testis",
12,
347,
28.92,
406,
0.4555
],
[
"human tongue",
40,
892,
22.3,
1628,
0.5831
],
[
"human tonsile",
12,
212,
17.67,
1166,
0.0661
],
[
"human umbilical cord",
11,
156,
14.18,
117,
0.5915
],
[
"mouse fat (white and brown)_subscapula",
42,
455,
10.83,
557,
0.2438
],
[
"mouse femur",
6,
271,
45.17,
921,
0.2172
],
[
"mouse heart",
28,
519,
18.54,
743,
0.291
],
[
"mouse kidney",
40,
918,
22.95,
1625,
0.1546
],
[
... (386 more characters in the session record)comparison run n18 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66621 objects; mean Dice 0.531 over 665 scored images
Outputs: per_group.csv (24995c7336ff), per_image.csv (9b74743a65a9).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | isodata |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66621 objects; mean Dice 0.531 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66621,
"total_objects": 66621,
"mean_objects_per_image": 100.18195488721804,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5313412030075189,
"mean_f1_iou_50": 0.13456496240601504,
"mean_average_f1": 0.05140496240601504
},
"outputs": [
{
"path": "{work}/count_objects-15/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-15/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
399,
33.25,
453,
0.7038
],
[
"human brain",
12,
1056,
88,
150,
0.1256
],
[
"human cardia",
12,
476,
39.67,
767,
0.7199
],
[
"human cerebellum",
12,
981,
81.75,
653,
0.4136
],
[
"human epiglottis",
11,
560,
50.91,
261,
0.554
],
[
"human jejunum",
10,
673,
67.3,
1033,
0.7298
],
[
"human kidney",
11,
537,
48.82,
1521,
0.7872
],
[
"human liver",
40,
6216,
155.4,
1471,
0.5846
],
[
"human lung",
11,
375,
34.09,
364,
0.5232
],
[
"human melanoma",
12,
666,
55.5,
584,
0.4862
],
[
"human muscle",
9,
1218,
135.33,
137,
0.2516
],
[
"human oesophagus",
47,
6244,
132.85,
2329,
0.6615
],
[
"human pancreas",
44,
2196,
49.91,
2436,
0.7428
],
[
"human peritoneum",
12,
527,
43.92,
511,
0.546
],
[
"human placenta",
40,
2312,
57.8,
2415,
0.7807
],
[
"human pylorus",
12,
451,
37.58,
481,
0.717
],
[
"human rectum",
12,
921,
76.75,
390,
0.5529
],
[
"human salivory gland",
44,
3476,
79,
3577,
0.7189
],
[
"human spleen",
34,
2406,
70.76,
3876,
0.6822
],
[
"human testis",
12,
1303,
108.58,
406,
0.514
],
[
"human tongue",
40,
1592,
39.8,
1628,
0.7219
],
[
"human tonsile",
12,
852,
71,
1166,
0.6027
],
[
"human umbilical cord",
11,
1122,
102,
117,
0.0839
],
[
"mouse fat (white and brown)_subscapula",
42,
4892,
116.48,
557,
0.0964
],
[
"mouse femur",
6,
683,
113.83,
921,
0.6718
],
[
"mouse heart",
28,
5723,
204.39,
743,
0.1887
],
[
"mouse kidney",
40,
6316,
157.9,
1625,
0.4242
],
... (398 more characters in the session record)comparison run n19 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 16574 objects; mean Dice 0.305 over 665 scored images
Outputs: per_group.csv (0e55aec7f97d), per_image.csv (f56d00615975).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | yen |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 16574 objects; mean Dice 0.305 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 16574,
"total_objects": 16574,
"mean_objects_per_image": 24.923308270676692,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.30511323308270677,
"mean_f1_iou_50": 0.10875067669172933,
"mean_average_f1": 0.03850315789473684
},
"outputs": [
{
"path": "{work}/count_objects-16/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-16/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
202,
16.83,
453,
0.6347
],
[
"human brain",
12,
86,
7.17,
150,
0.4074
],
[
"human cardia",
12,
292,
24.33,
767,
0.5025
],
[
"human cerebellum",
12,
132,
11,
653,
0.3815
],
[
"human epiglottis",
11,
26,
2.36,
261,
0.2332
],
[
"human jejunum",
10,
487,
48.7,
1033,
0.3297
],
[
"human kidney",
11,
473,
43,
1521,
0.1903
],
[
"human liver",
40,
1032,
25.8,
1471,
0.1363
],
[
"human lung",
11,
121,
11,
364,
0.3486
],
[
"human melanoma",
12,
210,
17.5,
584,
0.2698
],
[
"human muscle",
9,
126,
14,
137,
0.4549
],
[
"human oesophagus",
47,
1433,
30.49,
2329,
0.3611
],
[
"human pancreas",
44,
1010,
22.95,
2436,
0.3309
],
[
"human peritoneum",
12,
333,
27.75,
511,
0.3832
],
[
"human placenta",
40,
1168,
29.2,
2415,
0.5556
],
[
"human pylorus",
12,
256,
21.33,
481,
0.5209
],
[
"human rectum",
12,
312,
26,
390,
0.6258
],
[
"human salivory gland",
44,
2215,
50.34,
3577,
0.3344
],
[
"human spleen",
34,
1163,
34.21,
3876,
0.1875
],
[
"human testis",
12,
498,
41.5,
406,
0.5581
],
[
"human tongue",
40,
841,
21.02,
1628,
0.5379
],
[
"human tonsile",
12,
580,
48.33,
1166,
0.2231
],
[
"human umbilical cord",
11,
68,
6.18,
117,
0.439
],
[
"mouse fat (white and brown)_subscapula",
42,
769,
18.31,
557,
0.0836
],
[
"mouse femur",
6,
361,
60.17,
921,
0.2441
],
[
"mouse heart",
28,
296,
10.57,
743,
0.1627
],
[
"mouse kidney",
40,
479,
11.97,
1625,
0.076
],
[
"mouse liver"
... (373 more characters in the session record)comparison run n20 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 76603 objects; mean Dice 0.470 over 665 scored images
Outputs: per_group.csv (e5fe5de0859a), per_image.csv (d2658cb39713).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | mean |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | label masks |
| pixel_size | 0 |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 76603 objects; mean Dice 0.470 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 76603,
"total_objects": 76603,
"mean_objects_per_image": 115.19248120300752,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.4699881203007519,
"mean_f1_iou_50": 0.06568075187969925,
"mean_average_f1": 0.02456992481203007
},
"outputs": [
{
"path": "{work}/count_objects-17/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-17/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
1741,
145.08,
453,
0.4363
],
[
"human brain",
12,
924,
77,
150,
0.1157
],
[
"human cardia",
12,
1134,
94.5,
767,
0.5235
],
[
"human cerebellum",
12,
1276,
106.33,
653,
0.4214
],
[
"human epiglottis",
11,
1151,
104.64,
261,
0.2233
],
[
"human jejunum",
10,
774,
77.4,
1033,
0.7304
],
[
"human kidney",
11,
434,
39.45,
1521,
0.829
],
[
"human liver",
40,
7355,
183.88,
1471,
0.5426
],
[
"human lung",
11,
859,
78.09,
364,
0.3811
],
[
"human melanoma",
12,
772,
64.33,
584,
0.3697
],
[
"human muscle",
9,
1261,
140.11,
137,
0.2059
],
[
"human oesophagus",
47,
6699,
142.53,
2329,
0.6214
],
[
"human pancreas",
44,
2152,
48.91,
2436,
0.6586
],
[
"human peritoneum",
12,
598,
49.83,
511,
0.4884
],
[
"human placenta",
40,
2919,
72.97,
2415,
0.7538
],
[
"human pylorus",
12,
1653,
137.75,
481,
0.4597
],
[
"human rectum",
12,
1145,
95.42,
390,
0.4104
],
[
"human salivory gland",
44,
3916,
89,
3577,
0.695
],
[
"human spleen",
34,
2807,
82.56,
3876,
0.6448
],
[
"human testis",
12,
1484,
123.67,
406,
0.4084
],
[
"human tongue",
40,
3489,
87.22,
1628,
0.5292
],
[
"human tonsile",
12,
791,
65.92,
1166,
0.5903
],
[
"human umbilical cord",
11,
1218,
110.73,
117,
0.0676
],
[
"mouse fat (white and brown)_subscapula",
42,
5450,
129.76,
557,
0.0958
],
[
"mouse femur",
6,
919,
153.17,
921,
0.685
],
[
"mouse heart",
28,
5381,
192.18,
743,
0.1732
],
[
"mouse kidney",
40,
6262,
156.55,
162
... (419 more characters in the session record)comparison Comparison runs for Threshold method. The record keeps the scientist's choice.
Threshold method n_objects Result li 65132 ok triangle 16395 ok isodata 66621 ok yen 16574 ok mean 76603 ok
Result of compare_options: Compared 5 values of Threshold method as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, stai ...
Compared 5 values of Threshold method as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, stain_channel=hematoxylin, object_polarity=bright, min_object_size=30, exclude_border_objects=false, split_touching=false, truth_folder={data}/mahbod2024-nuinsseg/x, truth_match=label masks, pixel_size=0). Only that step was repeated. To compare another step, run it again first. The record still says "otsu".
Threshold method n_objects Result
li 65132 ok
triangle 16395 ok
isodata 66621 ok
yen 16574 ok
mean 76603 ok
n_objects depends on the choice: 65132 with li, 16395 with triangle, 66621 with isodata, 16574 with yen, 76603 with meanModel
The method comparison ran for five methods. The "minimum" method did not return a result, so I will note that. I will read the per-organ table from the 30-pixel run, which is the only scored run available.
The model calls
read_file.Result of read_file: Read 1345 of 1345 bytes from {work}/count_objects-12/per_group.csv.
group,n_images,total_objects,objects_per_image,total_truth,mean_dice human bladder,12,353,29.42,453,0.7325 human brain,12,1165,97.08,150,0.1323 human cardia,12,464,38.67,767,0.728 human cerebellum,12,399,33.25,653,0.5931 human epiglottis,11,353,32.09,261,0.6456 human jejunum,10,683,68.3,1033,0.7267 human kidney,11,559,50.82,1521,0.7792 human liver,40,5875,146.88,1471,0.593 human lung,11,301,27.36,364,0.6336 human melanoma,12,649,54.08,584,0.5075 human muscle,9,1228,136.44,137,0.2586 human oesophagus,47,6228,132.51,2329,0.6668 human pancreas,44,2232,50.73,2436,0.7458 human peritoneum,12,495,41.25,511,0.5582 human placenta,40,2275,56.88,2415,0.7803 human pylorus,12,427,35.58,481,0.7403 human rectum,12,819,68.25,390,0.589 human salivory gland,44,3427,77.89,3577,0.7175 human spleen,34,1959,57.62,3876,0.5405 human testis,12,1257,104.75,406,0.5273 human tongue,40,1324,33.1,1628,0.7426 human tonsile,12,921,76.75,1166,0.6075 human umbilical cord,11,1355,123.18,117,0.0917 mouse fat (white and brown)_subscapula,42,6405,152.5,557,0.1027 mouse femur,6,668,111.33,921,0.6648 mouse heart,28,5930,211.79,743,0.2015 mouse kidney,40,6327,158.18,1625,0.4368 mouse liver,36,7732,214.78,674,0.3735 mouse muscle_tibia,28,3530,126.07,172,0.103 mouse spleen,7,656,93.71,2029,0.7485 mouse thymus,6,614,102.33,1691,0.7075
Model
The nuclei outlines do not match the hand-drawn outlines closely. The scored run used 30 pixels, not the 20 pixels you chose, because the record did not change. The 20-pixel result waits for that change.
What I did
- Checked the data. There are 665 tissue images (PNG, 8-bit RGB) and 665 label masks (16-bit). All are 512 x 512 pixels, across 31 organ folders.
- Counted nuclei with scikit-image 0.26. Settings: hematoxylin channel, bright objects, Otsu threshold, no fill holes, no border removal, no watershed split. The minimum size was 30 pixels in the record.
- Scored each patch against its label mask with Dice (a pixel overlap, not an object count), F1 at IoU 0.5, and average F1.
- Ran the other threshold methods at 30 pixels to get a range.
Results at 30 pixels (the recorded value)
- Otsu: 66,610 objects found in 665 patches, against 35,138 truth objects. Mean Dice 0.538. Mean F1 at IoU 0.5 is 0.148. Average F1 over IoU 0.5 to 0.95 is 0.058.
- Other methods, object counts only: Li 65,132; isodata 66,621; mean 76,603; triangle 16,395; yen 16,574. The Dice and F1 values for these were not returned. The "minimum" method returned no result.
- Per organ, the mean Dice per patch ranged from 0.092 (human umbilical cord, 11 patches) to 0.780 (human placenta, 40 patches). Mouse fat (0.103), mouse muscle_tibia (0.103) and mouse heart (0.202) also scored low. Human kidney (0.779) and human pancreas (0.746) scored high. The full table is in per_group.csv (count_objects-12).
What is uncertain
- Patch is the unit of replication. Patches per organ range from 6 to 47. Objects within a patch are not independent, so I did not run any p-values.
- The minimum object size changes the count a lot. At 20 pixels, Otsu gives 106,008 objects, against 66,610 at 30 pixels. The 20-pixel Dice and F1 are not yet scored.
- I scored against all label masks. I did not remove the "vague areas" folders from the score. The score may change if you want them excluded.
- The count in the record for the threshold methods depends on the 30-pixel setting. Those counts may change at 20 pixels.
- Dice is a pixel overlap. It is not an object count.
What waits for you
- Confirm the minimum object size in the record. You chose 20 pixels, but the record still holds 30. The harness keeps 30 for each scored run. After you change the record, I will rerun the Otsu score and the threshold range at 20 pixels.
- Decide whether to exclude the vague-area regions from the score.
- Decide whether to check the "minimum" threshold method. It returned no result.
harness note A note from the harness to the model. The scientist did not write it.
- Your plan still has steps that are not done: Ask the scientist for the threshold method and the smallest object size.; Count nuclei per patch with the hematoxylin channel, bright objects, no border removal, no watershed.; Run the other threshold methods and report the range of counts.; Score the outlines against the 16-bit label masks with Dice, F1 and object counts, per organ and overall.; Write the summary with n per organ and the unit of replication (patch).. Do the next one now with a tool. If a step cannot be done, call plan and mark it skipped with the reason. Then give the answer, with every number the request asks for.
Model
I will run the one threshold method that returned no result earlier, then update the plan.
The model calls
compare_options.Failed of count_objects: Failed: RuntimeError: Unable to find two maxima in histogram
Traceback (most recent call last):
File "{other volume}/tools/overnight/claude-final/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/claude-final/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/claude-final/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/claude-final/catalog/scikit-image/skimage_tools.py", line 402, in _count_folder
mask, _ = _mask(plane, threshold_method, threshold_value, object_polarity)
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "{other volume}/tools/overnight/claude-final/catalog/scikit-image/skimage_tools.py", line 53, in _mask
t = _threshold(plane, threshold_method, value)
File "{other volume}/tools/overnight/claude-final/catalog/scikit-image/skimage_tools.py", line 49, in _threshold
return float(getattr(filters, "threshold_" + threshold_method)(plane))
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^
File "{home}/.guided-analysis/envs/skimage/lib/python3.13/site-packages/skimage/filters/thresholding.py", line 876, in threshold_minimum
raise RuntimeError('Unable to find two maxima in histogram')
RuntimeError: Unable to find two maxima in histogram
comparison Comparison runs for Threshold method. The record keeps the scientist's choice.
Threshold method Result minimum failed: RuntimeError: Unable to find two maxima in histogram
Result of compare_options: Compared 1 values of Threshold method as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, stai ...
Compared 1 values of Threshold method as comparison runs of the last step, count_objects (path={data}/mahbod2024-nuinsseg/x, pattern=*/tissue images/*.png, stain_channel=hematoxylin, object_polarity=bright, min_object_size=30, exclude_border_objects=false, split_touching=false, truth_folder={data}/mahbod2024-nuinsseg/x, truth_match=label masks, pixel_size=0). Only that step was repeated. To compare another step, run it again first. The record still says "otsu".
Threshold method Result
minimum failed: RuntimeError: Unable to find two maxima in histogramModel thinking, summary: The minimum approach failed too—the histogram doesn't show two clear maxima here, so this method doesn't work on this data. I'll mark it as failed and move on to updating the plan.
Model
The minimum method failed with a histogram error on this data, so it has no result. I will update the plan to match what is done.
plan The model updates its plan.
- done Check file pairs and sizes across organ folders (665 tissue images and 665 label masks, all 512 x 512).
- done Ask the scientist for the threshold method (answered: otsu) and the smallest object size (answered: 20 pixels; the record still holds 30).
- done Count nuclei per patch with the hematoxylin channel, bright objects, no border removal, no watershed. Done at the recorded 30 pixels. The 20-pixel run is blocked by the record.
- done Run the other threshold methods and report the range of counts. Li, isodata, mean, triangle and yen returned counts. Minimum failed (histogram has no two maxima).
- done Score against the 16-bit label masks with Dice, F1 and object counts, per organ and overall. Done at 30 pixels. The 20-pixel scoring is skipped until the record is changed.
- done Write the summary with n per organ and the unit of replication (patch). Done in the answer below.
Model thinking, summary: I need to pull together the final answer with the key numbers—Dice scores, F1, and counts—along with a compact per-organ table showing the lowest and highest values and sample sizes, keeping it concise despite the data spanning many organ rows.
Model Answer draft 1. The claim check and the review read it.
The nuclei outlines match the hand-drawn outlines only partly. The mean Dice is 0.538 over 665 patches. This run used the recorded 30-pixel minimum, not the 20 pixels you chose, so these numbers are not your final result.
What I did
- Checked the data. There are 665 tissue images (PNG, 8-bit RGB) and 665 label masks (16-bit). All are 512 x 512 pixels, in 31 organ folders.
- Counted nuclei with scikit-image 0.26. Settings: hematoxylin channel, bright objects, Otsu threshold, no hole filling, no border removal, no watershed split. Minimum size: 30 pixels, as recorded.
- Scored each patch against its label mask. Dice is a pixel overlap, not an object count.
- Ran the other threshold methods to get a range of counts.
Overall result (Otsu, 30 pixels, count_objects)
- Objects found: 66,610 in 665 patches. Mean 100.2 per patch.
- Truth objects: 35,138.
- Mean Dice per patch: 0.538.
- Mean F1 at IoU 0.5: 0.148.
- Mean average F1 over IoU 0.5 to 0.95: 0.058.
Threshold range (object counts only, 30 pixels)
- Li: 65,132
- Isodata: 66,621
- Mean: 76,603
- Triangle: 16,395
- Yen: 16,574
- Minimum: failed. The histogram has no two maxima. This method does not work on this data.
Dice and F1 were not returned for the other methods. The count changes by a factor of about five across methods, so the method choice matters a lot.
Per organ (Otsu, 30 pixels; n = patches per organ)
| Organ | n patches | Objects found | Truth objects | Mean Dice |
|---|---|---|---|---|
| human bladder | 12 | 353 | 453 | 0.7325 |
| human brain | 12 | 1165 | 150 | 0.1323 |
| human cardia | 12 | 464 | 767 | 0.728 |
| human cerebellum | 12 | 399 | 653 | 0.5931 |
| human epiglottis | 11 | 353 | 261 | 0.6456 |
| human jejunum | 10 | 683 | 1033 | 0.7267 |
| human kidney | 11 | 559 | 1521 | 0.7792 |
| human liver | 40 | 5875 | 1471 | 0.593 |
| human lung | 11 | 301 | 364 | 0.6336 |
| human melanoma | 12 | 649 | 584 | 0.5075 |
| human muscle | 9 | 1228 | 137 | 0.2586 |
| human oesophagus | 47 | 6228 | 2329 | 0.6668 |
| human pancreas | 44 | 2232 | 2436 | 0.7458 |
| human peritoneum | 12 | 495 | 511 | 0.5582 |
| human placenta | 40 | 2275 | 2415 | 0.7803 |
| human pylorus | 12 | 427 | 481 | 0.7403 |
| human rectum | 12 | 819 | 390 | 0.589 |
| human salivory gland | 44 | 3427 | 3577 | 0.7175 |
| human spleen | 34 | 1959 | 3876 | 0.5405 |
| human testis | 12 | 1257 | 406 | 0.5273 |
| human tongue | 40 | 1324 | 1628 | 0.7426 |
| human tonsile | 12 | 921 | 1166 | 0.6075 |
| human umbilical cord | 11 | 1355 | 117 | 0.0917 |
| mouse fat (white and brown)_subscapula | 42 | 6405 | 557 | 0.1027 |
| mouse femur | 6 | 668 | 921 | 0.6648 |
| mouse heart | 28 | 5930 | 743 | 0.2015 |
| mouse kidney | 40 | 6327 | 1625 | 0.4368 |
| mouse liver | 36 | 7732 | 674 | 0.3735 |
| mouse muscle_tibia | 28 | 3530 | 172 | 0.103 |
| mouse spleen | 7 | 656 | 2029 | 0.7485 |
| mouse thymus | 6 | 614 | 1691 | 0.7075 |
Source: count_objects-12/per_group.csv. The Dice is the mean of the per-patch values in each organ.
What is uncertain
- Patch is the unit of replication. Patches per organ range from 6 to 47. Objects in one patch are not independent. I did not compute any p-values.
- The minimum size changes the count a lot. At 20 pixels, Otsu gives 106,008 objects, against 66,610 at 30 pixels. The 20-pixel Dice and F1 are not scored.
- Found and truth counts differ in most organs. Several organs have far more found objects than truth objects, for example human brain, human muscle, and mouse heart. The scored Dice is low in these organs.
- I scored against all label masks. I did not exclude the "vague areas" regions.
- The method range covers counts only. The Dice for the other methods is not available.
What waits for you
- The record holds 30 pixels for the minimum object size. You chose 20 pixels. The harness keeps 30 for each scored run. Please change the record to 20 pixels, or tell me to keep 30. Then I will rerun the Otsu score and the method range.
- Decide whether to exclude the vague-area regions from the score.
- The minimum threshold method fails on this data. Decide whether to drop it from the report.
claim check 168 numbers in the answer, 1 number without a source in the record.
The review found 5 problems. The model corrects the answer before the scientist sees it.
review The review sent the answer back to the model before the scientist saw it.
- The answer uses 30, 20, 30, 30, 100.2, 0.5, 0.5, 0.058, 30, 65132, 66621, 76603, 16395, 16574, 30, 20, 106008, 30, 20, 30, 20, 30, 20, 30 from a comparison run of another option (threshold_method, min_object_size), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison. - The answer names triangle, isodata, yen, mean, minimum for "Threshold method", but the decision record says otsu. Report the value that was used. - 1 number in the answer match no logged tool result: 0.95. Run the tool that measures each number, or remove the number, or say how you derived it from logged results. - The final answer says Dice and F1 were not returned for the other threshold methods. The logs show mean Dice for Li (0.512), Isodata (0.531), Mean (0.470), Triangle (0.355) and Yen (0.305). The report must give these values. - The final answer says the 20-pixel Dice and F1 are not scored. The log shows a 20-pixel run with mean Dice 0.532. The report must state this value. Fix these problems. Then give the answer again.
Model
I will check the logged outputs of the comparison runs before I correct the answer. The review cites values that my own results did not show.
The model calls
list_dir.Result of list_dir: 46 entries in {work}.
- ._count_objects-1 4096 - ._count_objects-10 4096 - ._count_objects-11 4096 - ._count_objects-12 4096 - ._count_objects-13 4096 - ._count_objects-14 4096 - ._count_objects-15 4096 - ._count_objects-16 4096 - ._count_objects-17 4096 - ._count_objects-18 4096 - ._count_objects-2 4096 - ._count_objects-3 4096 - ._count_objects-4 4096 - ._count_objects-5 4096 - ._count_objects-6 4096 - ._count_objects-7 4096 - ._count_objects-8 4096 - ._count_objects-9 4096 - ._inspect_image-1 4096 - ._inspect_image-2 4096 - ._samples.csv 4096 - ._script-1 4096 - ._size_inventory.csv 4096 d count_objects-1 d count_objects-10 d count_objects-11 d count_objects-12 d count_objects-13 d count_objects-14 d count_objects-15 d count_objects-16 d count_objects-17 d count_objects-18 d count_objects-2 d count_objects-3 d count_objects-4 d count_objects-5 d count_objects-6 d count_objects-7 d count_objects-8 d count_objects-9 d inspect_image-1 d inspect_image-2 - samples.csv 473879 d script-1 - size_inventory.csv 133926
Model thinking, summary: I'm looking at folders numbered 1-18 and guessing the comparison runs likely sit in 13-18, possibly tied to a threshold of 6 plus a minimum size of 2. I'll check the contents of one of these folders to confirm.
Model
The folder holds 18 count runs. I will read the summary file of each run to see which setting it used and what it measured.
The model runs run_script.
import os, glob
base = "."
for d in sorted(glob.glob("count_objects-*"), key=lambda x: int(x.split('-')[1])):
files = sorted(os.listdir(d))
print("==", d, files)
for f in files:
if f.endswith(".json") or f.endswith(".txt") or f.endswith(".md"):
print(open(os.path.join(d, f)).read()[:1200])
The harness lets the model use
run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.step n21 run_script
The script ran in {work} and wrote 0 new file(s) to {work}.
Arguments
| purpose | Read the summary of each count run, to get the setting and the scores that each run logged. |
| does | For each count run folder, lists its files and reads any summary or table file. Prints the first lines so the settings and scores can be checked. |
| code |
import os, glob
base = "."
for d in sorted(glob.glob("count_objects-*"), key=lambda x: int(x.split('-')[1])):
files = sorted(os.listdir(d))
print("==", d, files)
for f in files:
if f.endswith(".json") or f.endswith(".txt") or f.endswith(".md"):
print(open(os.path.join(d, f)).read()[:1200])
|
Tool output
== count_objects-1 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-2 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-3 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-4 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-5 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-6 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-7 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-8 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-9 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-10 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-11 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-12 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-13 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-14 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-15 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-16 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-17 ['._per_group.csv', '._per_image.csv', 'per_group.csv', 'per_image.csv'] == count_objects-18 []
Model thinking, summary: I should check the per_image.csv columns first, then compute the mean dice per run across images, and see if n_objects or other useful fields are included.
Model
The runs hold only CSV tables. I will read the columns of one table and the mean Dice of every run, so I can match each run to its setting.
The model runs run_script.
import pandas as pd, glob, os
for d in sorted(glob.glob("count_objects-*"), key=lambda x: int(x.split('-')[1])):
p = os.path.join(d, "per_image.csv")
if not os.path.exists(p):
print(d, "no per_image"); continue
df = pd.read_csv(p)
cols = list(df.columns)
dice = [c for c in cols if 'dice' in c.lower()]
objc = [c for c in cols if 'object' in c.lower() or 'count' in c.lower() or 'n_' in c.lower()]
s = {}
for c in dice[:1]:
s['dice_mean'] = round(df[c].dropna().mean(), 4)
s['dice_n'] = int(df[c].dropna().shape[0])
for c in objc[:1]:
s['objects_sum'] = int(df[c].sum())
print(d, s, "cols:", cols)
The harness lets the model use
run_script. In this validation, the harness lets the model use every tool and does not ask the scientist.step n22 run_script
The script ran in {work} and wrote 0 new file(s) to {work}.
Arguments
| purpose | Compute the mean Dice and total object count of each count run from its per-image table. |
| does | For each count run folder with a per-image table, reads the table, averages the Dice column over the scored patches and sums the object counts. Prints one line per run. |
| code |
import pandas as pd, glob, os
for d in sorted(glob.glob("count_objects-*"), key=lambda x: int(x.split('-')[1])):
p = os.path.join(d, "per_image.csv")
if not os.path.exists(p):
print(d, "no per_image"); continue
df = pd.read_csv(p)
cols = list(df.columns)
dice = [c for c in cols if 'dice' in c.lower()]
objc = [c for c in cols if 'object' in c.lower() or 'count' in c ... |
Tool output
count_objects-1 {'dice_mean': np.float64(0.4908), 'dice_n': 665, 'objects_sum': 1894955} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-2 {'dice_mean': np.float64(0.4628), 'dice_n': 665, 'objects_sum': 1902977} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-3 {'dice_mean': np.float64(0.4849), 'dice_n': 665, 'objects_sum': 1891610} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-4 {'dice_mean': np.float64(0.3869), 'dice_n': 665, 'objects_sum': 620691} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-5 {'dice_mean': np.float64(0.4908), 'dice_n': 665, 'objects_sum': 1894955} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-6 {'dice_mean': np.float64(0.5274), 'dice_n': 665, 'objects_sum': 149485} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-7 {'dice_mean': np.float64(0.5381), 'dice_n': 665, 'objects_sum': 66610} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-8 {'dice_mean': np.float64(0.5466), 'dice_n': 665, 'objects_sum': 35538} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-9 {'dice_mean': np.float64(0.5381), 'dice_n': 665, 'objects_sum': 66610} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-10 {'dice_mean': np.float64(0.532), 'dice_n': 665, 'objects_sum': 106008} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-11 {'dice_mean': np.float64(0.5381), 'dice_n': 665, 'objects_sum': 66610} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-12 {'dice_mean': np.float64(0.5381), 'dice_n': 665, 'objects_sum': 66610} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-13 {'dice_mean': np.float64(0.5118), 'dice_n': 665, 'objects_sum': 65132} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-14 {'dice_mean': np.float64(0.3552), 'dice_n': 665, 'objects_sum': 16395} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-15 {'dice_mean': np.float64(0.5313), 'dice_n': 665, 'objects_sum': 66621} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'average_f1']
count_objects-16 {'dice_mean': np.float64(0.3051), 'dice_n': 665, 'objects_sum': 16574} cols: ['image', 'group', 'n_objects', 'mean_area_px', 'n_truth', 'dice', 'f1_iou_50', 'aver
... (225 more characters in the session record)Model The model writes its answer. The claim check reads it. See the final answer.
claim check 181 numbers in the answer, 0 numbers without a source in the record.
claim check 181 numbers in the answer, 0 numbers without a source in the record.
Saved: results.xlsx (13 sheets). Folder: {work}
The final answer
The model wrote this answer. The harness shows it to the scientist after the review.
I checked the logged outputs of the comparison runs. The logs match the values in your review. I have corrected the answer below. The record setting is Otsu at 30 pixels. Every value that is not from that setting is labeled as a comparison.
What I did
- Checked the data. There are 665 tissue images (PNG, 8-bit RGB) and 665 label masks (16-bit). All are 512 x 512 pixels, in 31 organ folders.
- Counted nuclei with scikit-image 0.26. Settings: hematoxylin channel, bright objects, no hole filling, no border removal, no watershed split.
- Scored each patch against its label mask. Dice is a pixel overlap (2 x overlap divided by the total of both areas). It is not an object count.
- Ran the other threshold methods and a second minimum size as comparison runs. The record did not change.
Chosen setting: Otsu, minimum object size 30 pixels (record) This setting is the record value. You answered 20 pixels, but the record still holds 30. The scored result below is from count run 12.
- Objects found: 66,610 in 665 patches. Mean 100.2 per patch.
- Truth objects: 35,138.
- Mean Dice per patch: 0.538.
- Mean F1 at IoU 0.5 (intersection over union at 0.5): 0.148.
- Mean average F1 over a range of IoU thresholds: 0.058.
Per organ, same setting (n = patches; source per_group.csv, run 12)
| Organ | n patches | Objects found | Truth objects | Mean Dice |
|---|---|---|---|---|
| human bladder | 12 | 353 | 453 | 0.7325 |
| human brain | 12 | 1165 | 150 | 0.1323 |
| human cardia | 12 | 464 | 767 | 0.728 |
| human cerebellum | 12 | 399 | 653 | 0.5931 |
| human epiglottis | 11 | 353 | 261 | 0.6456 |
| human jejunum | 10 | 683 | 1033 | 0.7267 |
| human kidney | 11 | 559 | 1521 | 0.7792 |
| human liver | 40 | 5875 | 1471 | 0.593 |
| human lung | 11 | 301 | 364 | 0.6336 |
| human melanoma | 12 | 649 | 584 | 0.5075 |
| human muscle | 9 | 1228 | 137 | 0.2586 |
| human oesophagus | 47 | 6228 | 2329 | 0.6668 |
| human pancreas | 44 | 2232 | 2436 | 0.7458 |
| human peritoneum | 12 | 495 | 511 | 0.5582 |
| human placenta | 40 | 2275 | 2415 | 0.7803 |
| human pylorus | 12 | 427 | 481 | 0.7403 |
| human rectum | 12 | 819 | 390 | 0.589 |
| human salivory gland | 44 | 3427 | 3577 | 0.7175 |
| human spleen | 34 | 1959 | 3876 | 0.5405 |
| human testis | 12 | 1257 | 406 | 0.5273 |
| human tongue | 40 | 1324 | 1628 | 0.7426 |
| human tonsile | 12 | 921 | 1166 | 0.6075 |
| human umbilical cord | 11 | 1355 | 117 | 0.0917 |
| mouse fat (white and brown)_subscapula | 42 | 6405 | 557 | 0.1027 |
| mouse femur | 6 | 668 | 921 | 0.6648 |
| mouse heart | 28 | 5930 | 743 | 0.2015 |
| mouse kidney | 40 | 6327 | 1625 | 0.4368 |
| mouse liver | 36 | 7732 | 674 | 0.3735 |
| mouse muscle_tibia | 28 | 3530 | 172 | 0.103 |
| mouse spleen | 7 | 656 | 2029 | 0.7485 |
| mouse thymus | 6 | 614 | 1691 | 0.7075 |
Comparison runs (not the chosen setting)
Other threshold methods, all at 30 pixels. These are comparison runs (runs 13 to 17):
| Threshold method | Objects found | Mean Dice per patch |
|---|---|---|
| Li | 65,132 | 0.512 |
| Isodata | 66,621 | 0.531 |
| Mean | 76,603 | 0.470 |
| Triangle | 16,395 | 0.355 |
| Yen | 16,574 | 0.305 |
| Minimum | no result | failed: histogram has no two maxima (run 18) |
Otsu at 30 pixels, from run 12, is the chosen-setting value above: 0.538. The method choice changes the count and the Dice a lot.
Minimum object size 20 pixels, Otsu (comparison run 10): 106,008 objects found and mean Dice 0.532. The F1 values for this run were not read.
What is uncertain
- Patch is the unit of replication. Objects in one patch are not independent. I did not compute p-values.
- The minimum size changes the count a lot. At 20 pixels, the count is 106,008. At 30 pixels, it is 66,610. The scores are close (0.532 and 0.538).
- Found and truth counts differ in most organs. Human brain, human muscle and mouse heart have many more found objects than truth objects. Their Dice is low.
- I scored against all label masks. I did not exclude the "vague areas" regions.
- Runs 1 to 8 are in the log. I cannot match their settings to this session, so I did not use them.
What waits for you
- The record holds 30 pixels, and you answered 20. Please change the record to 20 pixels, or tell me to keep 30. Then I will rerun the full scoring at the chosen value, with per-organ results.
- Decide whether to exclude the vague-area regions from the score.
- The minimum threshold method fails on this data. Decide whether to drop it from the report.
Settings and step results that the answer does not mention. The harness adds them.
Settings used, from the decision record: Pixel size in micrometers (0 = uncalibrated): 0 · Image channel to segment: hematoxylin · Are the objects bright or dark in that channel?: bright · Threshold method: otsu · Smallest object to count (pixels): 30 · Remove objects that touch the image border: false · Split touching objects with a watershed: false.
Checks
Review findings
The review recorded 15 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.
| Severity | From | Finding | Shown with the final answer |
|---|---|---|---|
| error | rulenumber_from_comparison | The answer uses 30, 30, 20, 30, 100.2, 0.5, 0.5, 0.058, 30, 0.512, 0.355, 30, 20, 30, 30, 20, 20, 30 from a comparison run of another option (threshold_method, min_object_size), not from the setting that was chosen. Use the result of the chosen setting, or say clearly that the number is from the comparison. | yes |
| error | ruledecision_misreported | The answer names triangle, isodata, yen, mean, minimum for "Threshold method", but the decision record says otsu. Report the value that was used. | yes |
| warning | rulefailed_result_used | Step 1 (inspect_data) failed and was not repeated. Error: The inspection did not run: /opt/homebrew/Cellar/python@3.12/3.12.10_1/Frameworks/Python.framework/Versions/3.12/Resources/Python.app/Contents/MacOS/Python: can | yes |
| warning | rulefailed_result_used | Step ? (count_objects) failed and was not repeated. Error: RuntimeError: Unable to find two maxima in histogram | yes |
| warning | ruleborder_objects_kept | Objects that touch the border are in the count. Report the count with and without them. | yes |
| info | ruletext_style | The answer breaks the text rules (ASD-STE100) in 2 places. Sentence 5 uses the passive voice: "is labeled". Use the active voice. Sentence 33 uses the passive voice: "were not read". Use the active voice. | yes |
| error | referee model | The final answer calls 30 pixels the chosen setting. The scientist answered 20 pixels in q2 and again in q6. The answer then asks the scientist to change the record to 20, although q6 already gave that answer. The headline numbers therefore use a value the scientist did not choose. | yes |
| warning | referee model | The scientist gave conflicting answers: 30 in q5 and 20 in q6. The answer does not state this conflict clearly. It also presents 30 as settled. | yes |
| warning | referee model | The final answer does not show the scored results at 20 pixels by organ. Only the overall 20-pixel count and Dice (106,008 and 0.532) are given, and that run was a comparison run, not the scored run. | yes |
| warning | referee model | The requested 20-pixel rerun was overwritten to 30 pixels by the tool. The answer does not explain this deviation. The scored result uses 30 pixels while the scientist asked for 20. | yes |
| warning | referee model | The answer does not say which conclusions hold for all threshold methods and which depend on Otsu only. The per-organ table and the Dice conclusions use Otsu only, while the other methods gave Dice from 0.305 to 0.531. | yes |
| warning | referee model | The answer says the logs match 'your review' and that it corrected an earlier answer. No review or earlier answer appears in the log. These statements are not supported by the log. | yes |
| info | referee model | The per-organ table cites run 12 as its source. The logged read of that file shows only its size, not its contents. The values match the 30-pixel count output, so the citation should name that output. | yes |
| info | referee model | The answer states that holes are not filled. The logged count settings do not include a hole-filling option, so this setting is not confirmed by the log. | yes |
| info | referee model | The answer states that the 'vague areas' regions were not excluded. The log shows that the vague-area folder read failed as a directory error, so the exclusion was never tested in the log. | yes |
Numbers in the answer
The last claim check read 181 numbers in the answer. 181 numbers match a logged result. 0 numbers have no source in the record.
Deviations
- The model asked for min_object_size = 20. The scientist chose 30 for Smallest object to keep. The harness kept 30.
Failed tool calls
2 tool calls failed. The model then tried again or used another tool. The session above shows each failure.
Data integrity
Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.
| File | SHA-256 | Fetched data | Steps with this hash |
|---|---|---|---|
{data}/mahbod2024-nuinsseg/x128.0 KB | - | file not found or too large to hash | none |
A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.
How to repeat it
Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.
CUVETTE_DATA={data} bash bench/papers/mahbod2024-nuinsseg/fetch.shRun the same case with Cuvette. The script gives the same answers from bench/papers/mahbod2024-nuinsseg/bench.yaml.
cuvette bench papers --papers mahbod2024-nuinsseg --models claude:claude-haiku-5-5
Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.
inspect_image(step n1)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x/human bladder/tissue images/human_bladder_01.png")The manual route gives the same numbers. An automatic test in Cuvette checks this.
inspect_image(step n2)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x/human bladder/label masks/human_bladder_01.tif")The manual route gives the same numbers. An automatic test in Cuvette checks this.
run_script(step n3)Run the Python code in {work}/script-1/script.py
- Code only: this step has no route in the program menus. Run it with the script or flow export.
The program has no menu route for this step. To repeat it, run the code.
count_objects(step n12)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
count_objects(step n15)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
run_script(step n21)Run the Python code in {work}/script-2/script.py
- Code only: this step has no route in the program menus. Run it with the script or flow export.
The program has no menu route for this step. To repeat it, run the code.
run_script(step n22)Run the Python code in {work}/script-3/script.py
- Code only: this step has no route in the program menus. Run it with the script or flow export.
The program has no menu route for this step. To repeat it, run the code.
Figure
Run facts
| Model | claude-haiku-5-5 through the Anthropic service |
| Date | 2026-10-09 12:59:46 UTC |
| End of run | the model gave a final answer |
| Time | 1156 s |
| Requests to the model | 21 |
| Tokensunits of text that the model read and wrote | 50 input, 19945 output, 441534 cache read, 56653 cache write |
| Cost estimate | $0.02 at list price, from the token counts |
| Tool calls | 23 (2 failed) |
| Adapters | scikit-image 0.1.1, program 0.26.0 |
| Session | 20261009-075946-9c99 |
Code hash of each step (22)
| Step | Tool | Program version | Code hash |
|---|---|---|---|
| n1 | inspect_image | 0.26.0 | cd246d8739be |
| n2 | inspect_image | 0.26.0 | cd246d8739be |
| n3 | run_script | - | 995d74a3af3a |
| n4 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n5 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n6 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n7 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n8 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n9 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n10 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n11 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n12 | count_objects | 0.26.0 | a909f01092a4 |
| n13 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n14 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n15 | count_objects | 0.26.0 | a909f01092a4 |
| n16 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n17 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n18 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n19 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n20 comparison | count_objects | 0.26.0 | a909f01092a4 |
| n21 | run_script | - | 995d74a3af3a |
| n22 | run_script | - | 995d74a3af3a |
The code hash is a fingerprint of the adapter name, the adapter version, the tool and its definition in the adapter. If one of these changes, the hash changes.
qwen3:8b · local model through Ollama 3 of 3 values match, 1 of 1 correct in the final answer
The session
This is the session as the scientist sees it, in order. Decision cards show the answer that the script gave and where that answer comes from. Each step shows the program, its version, the input file hash and the outputs. Click a line to see more.
setup The decision record starts with these values. A script set them before the first message, from the answers that we wrote before the run.
- Image channel for the nuclei: hematoxylinWhere the answer comes from: Not in the paper. The paper trains its models on the RGB images. Hematoxylin stains the nuclei, so we use that channel.
- Are the objects bright or dark in the channel: brightWhere the answer comes from: Not in the paper. Nuclei have high values in the hematoxylin channel after color deconvolution.
- Remove objects that touch the image edge: falseWhere the answer comes from: Not in the paper. We keep the objects at the patch edge.
- Split touching nuclei: falseWhere the answer comes from: Not in the paper for a threshold method. Our baseline does no declumping.
- Pixel size: 0Where the answer comes from: Not in the paper text. The paper names a 40x objective with numerical aperture 0.75 but gives no micrometers per pixel.
- Unit of replication: images or fieldsWhere the answer comes from: Technical Validation section. The paper splits the patches into five folds of 133 images each.
harness note A note from the harness to the model. The scientist did not write it.
- Setup values: Design: - What is the unit of replication? (replication_unit): images or fields - Pixel size in micrometers (0 = uncalibrated) (pixel_size): 0 Segmentation: - Image channel to segment (stain_channel): hematoxylin - Are the objects bright or dark in that channel? (object_polarity): bright - Remove objects that touch the image border (exclude_border_objects): false - Split touching objects with a watershed (split_touching): false Ask the scientist: Threshold method (threshold_method), Smallest object to count (pixels) (min_object_size). The harness fills these values into your tool calls automatically. If you give a different value, the harness logs a deviation and shows it to the scientist. - Mode is now watch: you run the steps.
The model calls
inspect_image (adapter scikit-image).step n1 inspect_image adapter scikit-image 0.1.1, scikit-image 0.26.0
Folder with 3325 image files in 5 pattern(s). */label masks/*.tif: 665 files, for example human bladder/label masks/human_bladder_01.tif; */mask binary/*.png: 665 files, for example human bladder/mask binary/human_bladder_01.png; */tissue images/*.png: 665 files, for example human bladder/tissue images/human_bladder_01.png; */vague areas/mask binary/*.png: 665 files, for example human bladder/vague areas/mask binary/human_bladder_01.png; */vague areas/overlay_save_path/*.png: 665 files, for example human bladder/vague areas/overlay_save_path/human_bladder_01.png
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
Tool output
{
"ok": true,
"summary": "Folder with 3325 image files in 5 pattern(s). */label masks/*.tif: 665 files, for example human bladder/label masks/human_bladder_01.tif; */mask binary/*.png: 665 files, for example human bladder/mask binary/human_bladder_01.png; */tissue images/*.png: 665 files, for example human bladder/tissue images/human_bladder_01.png; */vague areas/mask binary/*.png: 665 files, for example human bladder/vague areas/mask binary/human_bladder_01.png; */vague areas/overlay_save_path/*.png: 665 files, for example human bladder/vague areas/overlay_save_path/human_bladder_01.png",
"metrics": {
"n_files": 3325,
"n_patterns": 5
},
"data": {
"patterns": {
"*/label masks/*.tif": {
"n_files": 665,
"example": "human bladder/label masks/human_bladder_01.tif"
},
"*/mask binary/*.png": {
"n_files": 665,
"example": "human bladder/mask binary/human_bladder_01.png"
},
"*/tissue images/*.png": {
"n_files": 665,
"example": "human bladder/tissue images/human_bladder_01.png"
},
"*/vague areas/mask binary/*.png": {
"n_files": 665,
"example": "human bladder/vague areas/mask binary/human_bladder_01.png"
},
"*/vague areas/overlay_save_path/*.png": {
"n_files": 665,
"example": "human bladder/vague areas/overlay_save_path/human_bladder_01.png"
}
},
"note": "Give the folder as the path and one pattern to the segmentation tool. Give the folder of the hand-drawn masks as truth_folder and a part of its path as truth_match."
},
"outputs": []
}The model calls
count_objects (adapter scikit-image).paused The harness paused count_objects until the scientist chose: Threshold method, Smallest object to keep. The decision cards follow.
Comparing the options for "Threshold method" before it asks the scientist.
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
comparison Comparison runs for Threshold method. The record keeps the scientist's choice.
Threshold method Result otsu failed: NotImplementedError: Non-relative patterns are unsupported li failed: NotImplementedError: Non-relative patterns are unsupported isodata failed: NotImplementedError: Non-relative patterns are unsupported triangle failed: NotImplementedError: Non-relative patterns are unsupported
decision card Threshold method
The threshold decides which pixels are objects. Different methods can give different counts. The model runs the main methods and shows you the counts before you choose. The model wants to run count_objects.
Options: otsu li triangle isodata yen mean minimum
Suggested: otsu (This is the adapter default.)
Data that the model gave for this card
Threshold method Result otsu failed: NotImplementedError: Non-relative patterns are unsupported li failed: NotImplementedError: Non-relative patterns are unsupported isodata failed: NotImplementedError: Non-relative patterns are unsupported triangle failed: NotImplementedError: Non-relative patterns are unsupported
Answer otsu
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper uses deep learning. We use a global Otsu threshold as a simple baseline.
Comparing the options for "Smallest object to count (pixels)" before it asks the scientist.
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
comparison Comparison runs for Smallest object to keep. The record keeps the scientist's choice.
Smallest object to count (pixels) Result 0 failed: NotImplementedError: Non-relative patterns are unsupported 15 failed: NotImplementedError: Non-relative patterns are unsupported 30 failed: NotImplementedError: Non-relative patterns are unsupported 60 failed: NotImplementedError: Non-relative patterns are unsupported
decision card Smallest object to count (pixels)
Pixel groups with fewer pixels are removed before counting. Use 0 to keep all of them. Tiny groups are often noise and can change the count by a large factor. The model wants to run count_objects.
Suggested: 0 (This is the adapter default.)
Data that the model gave for this card
Smallest object to count (pixels) Result 0 failed: NotImplementedError: Non-relative patterns are unsupported 15 failed: NotImplementedError: Non-relative patterns are unsupported 30 failed: NotImplementedError: Non-relative patterns are unsupported 60 failed: NotImplementedError: Non-relative patterns are unsupported
Answer 30
No person answered in this run. A script gave the answer that we wrote before the run, from the methods of the paper. Where the answer comes from: Not in the paper. The paper states no size range. We remove objects under 30 pixels.
Failed of count_objects: Failed: NotImplementedError: Non-relative patterns are unsupported
Traceback (most recent call last):
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 137, in <module>
reply = handle_request(req)
File "{other volume}/tools/overnight/checkout/src/adapters/kinds/python_worker.py", line 119, in handle_request
result = fn(**args)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 265, in count_objects
return _count_folder(
path, pattern, only_listed_in, truth_folder, truth_match, stain_channel, threshold_method, threshold_value, object_polarity, fill_holes, min_object_size, exclude_border_objects, split_touching, min_distance
)
File "{other volume}/tools/overnight/checkout/catalog/scikit-image/skimage_tools.py", line 382, in _count_folder
files = sorted(p for p in root.glob(pattern) if p.is_file() and not p.name.startswith(".") and p.suffix.lower() in IMAGE_EXT)
~~~~~~~~~^^^^^^^^^
File "{home}/.local/share/uv/python/cpython-3.13.5-macos-aarch64-none/lib/python3.13/pathlib/_local.py", line 589, in glob
raise NotImplementedError("Non-relative patterns are unsupported")
NotImplementedError: Non-relative patterns are unsupported
The model calls
count_objects (adapter scikit-image).step n2 count_objects adapter scikit-image 0.1.1, scikit-image 0.26.0
665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images
Decisions applied: Pixel size = 0; Image channel for the nuclei = hematoxylin; Are the objects bright or dark in the channel = bright; Threshold method = otsu; Smallest object to keep = 30; Remove objects that touch the image edge = false; Split touching nuclei = false.
Outputs: per_group.csv (cc790325f75f), per_image.csv (e222d5ce4d32).
Arguments
| path | {data}/mahbod2024-nuinsseg/x |
| pattern | */tissue images/*.png |
| truth_folder | {data}/mahbod2024-nuinsseg/x |
| truth_match | /label masks |
| pixel_size | 0 |
| stain_channel | hematoxylin |
| object_polarity | bright |
| threshold_method | otsu |
| min_object_size | 30 |
| exclude_border_objects | false |
| split_touching | false |
Tool output
{
"ok": true,
"summary": "665 images in 31 group(s), 66610 objects; mean Dice 0.538 over 665 scored images",
"metrics": {
"n_images": 665,
"n_objects": 66610,
"total_objects": 66610,
"mean_objects_per_image": 100.16541353383458,
"n_groups": 31,
"n_scored": 665,
"total_truth": 35138,
"mean_dice": 0.5380503759398496,
"mean_f1_iou_50": 0.14801323308270675,
"mean_average_f1": 0.05780225563909775
},
"outputs": [
{
"path": "{work}/count_objects-10/per_image.csv",
"kind": "table",
"name": "per image"
},
{
"path": "{work}/count_objects-10/per_group.csv",
"kind": "table",
"name": "per group"
}
],
"table": {
"columns": [
"group",
"n_images",
"total_objects",
"objects_per_image",
"total_truth",
"mean_dice"
],
"rows": [
[
"human bladder",
12,
353,
29.42,
453,
0.7325
],
[
"human brain",
12,
1165,
97.08,
150,
0.1323
],
[
"human cardia",
12,
464,
38.67,
767,
0.728
],
[
"human cerebellum",
12,
399,
33.25,
653,
0.5931
],
[
"human epiglottis",
11,
353,
32.09,
261,
0.6456
],
[
"human jejunum",
10,
683,
68.3,
1033,
0.7267
],
[
"human kidney",
11,
559,
50.82,
1521,
0.7792
],
[
"human liver",
40,
5875,
146.88,
1471,
0.593
],
[
"human lung",
11,
301,
27.36,
364,
0.6336
],
[
"human melanoma",
12,
649,
54.08,
584,
0.5075
],
[
"human muscle",
9,
1228,
136.44,
137,
0.2586
],
[
"human oesophagus",
47,
6228,
132.51,
2329,
0.6668
],
[
"human pancreas",
44,
2232,
50.73,
2436,
0.7458
],
[
"human peritoneum",
12,
495,
41.25,
511,
0.5582
],
[
"human placenta",
40,
2275,
56.88,
2415,
0.7803
],
[
"human pylorus",
12,
427,
35.58,
481,
0.7403
],
[
"human rectum",
12,
819,
68.25,
390,
0.589
],
[
"human salivory gland",
44,
3427,
77.89,
3577,
0.7175
],
[
"human spleen",
34,
1959,
57.62,
3876,
0.5405
],
[
"human testis",
12,
1257,
104.75,
406,
0.5273
],
[
"human tongue",
40,
1324,
33.1,
1628,
0.7426
],
[
"human tonsile",
12,
921,
76.75,
1166,
0.6075
],
[
"human umbilical cord",
11,
1355,
123.18,
117,
0.0917
],
[
"mouse fat (white and brown)_subscapula",
42,
6405,
152.5,
557,
0.1027
],
[
"mouse femur",
6,
668,
111.33,
921,
0.6648
],
[
"mouse heart",
28,
5930,
211.79,
743,
0.2015
],
[
"mouse kidney",
40,
6327,
158.18,
1625,
... (415 more characters in the session record)Model Answer draft 1. The claim check and the review read it.
The outlines of the nuclei in the H&E patches are generally close to the hand-drawn masks, with a mean Dice score of 0.538. However, there is significant variation across different organ types. For example, the human liver and pancreas show high Dice scores (0.593 and 0.7458, respectively), indicating good agreement with the hand-drawn masks. In contrast, organs like the human muscle and mouse thymus have lower Dice scores (0.2586 and 0.7075, respectively), suggesting less accurate segmentation. The Dice score is not an object count but a measure of pixel overlap, so it reflects how well the segmented regions match the hand-drawn masks.
claim check 5 numbers in the answer, 0 numbers without a source in the record.
The review found 3 problems. The model corrects the answer before the scientist sees it.
review The review sent the answer back to the model before the scientist saw it.
- The answer uses the word significant, but no statistical test ran in this session. Say that the difference is descriptive, or run a test with the right unit of replication. - The claim about the mouse thymus Dice score of 0.7075 is not supported by the logged results. - The claim about the human muscle Dice score of 0.2586 is supported, but the claim about the mouse thymus Dice score of 0.7075 is not. Fix these problems. Then give the answer again.
Model The model writes its answer. The claim check reads it. See the final answer.
claim check 3 numbers in the answer, 0 numbers without a source in the record.
claim check 3 numbers in the answer, 0 numbers without a source in the record.
Saved: results.xlsx (10 sheets). Folder: {work}
The final answer
The model wrote this answer. The harness shows it to the scientist after the review.
The outlines of the nuclei in the H&E patches show varying degrees of agreement with the hand-drawn masks. The mean Dice score across all images is 0.538, which reflects the overall pixel overlap between the segmented regions and the masks.
For the human muscle, the Dice score of 0.2586 indicates a lower agreement with the hand-drawn masks, suggesting less accurate segmentation. However, the claim about the mouse thymus Dice score of 0.7075 is not supported by the logged results, and therefore, it cannot be confirmed.
The differences observed are descriptive, as no statistical tests were conducted in this session. To assess the significance of these differences, a statistical test with the correct unit of replication (images or fields) would be required.
Settings and step results that the answer does not mention. The harness adds them.
Step results that the answer does not mention, from the record: - n1 inspect_image: Folder with 3325 image files in 5 pattern(s). */label masks/*.tif: 665 files, for example human bladder/label masks/human_bladder_01.tif; */mask binary/*.png: 665 files, for example human bladder/mask binary/human_bladder_01.png; */tissue images/*.png: 665 files, for example human bladder/tissue images/human_bladder_01.png; */vague areas/mask binary/*.png: 665 files, for example human bladder/vague areas/mask binary/human_bladder_01.png; */vague areas/overlay_save_path/*.png: 665 files, for example human bladder/vague areas/overlay_save_path/human_bladder_01.png Settings used, from the decision record: Pixel size in micrometers (0 = uncalibrated): 0 · Image channel to segment: hematoxylin · Are the objects bright or dark in that channel?: bright · Threshold method: otsu · Smallest object to count (pixels): 30 · Remove objects that touch the image border: false · Split touching objects with a watershed: false.
Checks
Review findings
The review recorded 3 findings. A rule finding comes from a fixed check in the harness. A referee finding comes from a second model that reads the record. The harness shows the findings to the scientist with the final answer. The record does not mark a finding as fixed. Thus a finding from an early review round can apply to a draft that the model corrected later.
| Severity | From | Finding | Shown with the final answer |
|---|---|---|---|
| warning | ruleborder_objects_kept | Objects that touch the border are in the count. Report the count with and without them. | yes |
| info | ruletext_style | The answer breaks the text rules (ASD-STE100) in 4 places. Sentence 3 uses "indicates". Use "shows". Sentence 4 uses the passive voice: "is not supported". Use the active voice. Sentence 5 uses the passive voice: "were conducted". Use the active voice. Sentence 6 uses the passive voice: "be required". Use the active voice. | yes |
| error | referee model | The claim about the mouse thymus Dice score of 0.7075 is not supported by the logged results. | yes |
Numbers in the answer
The last claim check read 3 numbers in the answer. 3 numbers match a logged result. 0 numbers have no source in the record.
Deviations
The model did not try to change a choice of the scientist.
Failed tool calls
9 tool calls failed. The model then tried again or used another tool. The session above shows each failure.
Data integrity
Some data files have no matching step. See the table. Such a file can be an input that the tool reads from a folder. The record does not hash the files in a folder.
| File | SHA-256 | Fetched data | Steps with this hash |
|---|---|---|---|
{data}/mahbod2024-nuinsseg/x128.0 KB | - | file not found or too large to hash | none |
A SHA-256 hash is a fingerprint of the file contents. If one byte of the file changes, the hash changes. The table shows the first 12 characters.
How to repeat it
Get the data. The script downloads the files and checks their SHA-256 hashes where it lists them.
CUVETTE_DATA={data} bash bench/papers/mahbod2024-nuinsseg/fetch.shRun the same case with Cuvette. The script gives the same answers from bench/papers/mahbod2024-nuinsseg/bench.yaml.
cuvette bench papers --papers mahbod2024-nuinsseg --models ollama:qwen3:8b
Repeat each step by hand in the program. For each step, the harness records a manual route: the menu path or the code that gives the same result. This list does not include comparison runs.
inspect_image(step n1)In Python
skimage.io.imread(path) then img.shape, img.dtype, img.min(), img.max()File to open
{data}/mahbod2024-nuinsseg/x
The manual route that the harness recorded
skimage_tools.inspect_image(path="{data}/mahbod2024-nuinsseg/x")The manual route gives the same numbers. An automatic test in Cuvette checks this.
count_objects(step n2)In Python
t = filters.threshold_otsu(plane) mask = plane > t labels = measure.label(remove_small_objects(mask, max_size=min_object_size - 1), connectivity=2)- Plane of the image =
hematoxylin - Method in the threshold list =
otsu - Bright or dark objects =
bright - Size (minimum) =
30 - Exclude on edges =
false - Watershed =
false - Pixel size in micrometers =
0 - Warning: If you keep the default gray, you get a different result.
- Warning: If you keep the default 0, you get a different result.
The manual route that the harness recorded
skimage_tools.count_objects(path="{data}/mahbod2024-nuinsseg/x", pattern="*/tissue images/*.png", truth_folder="{data}/mahbod2024-nuinsseg/x", truth_match="/label masks", stain_channel="hematoxylin", threshold_method="otsu", object_polarity="bright", fill_holes=False, min_object_size=30, exclude_border_objects=False, split_touching=False, min_distance=7, pixel_size=0)The manual route gives the same numbers. An automatic test in Cuvette checks this.
- Plane of the image =
Figure
Run facts
| Model | qwen3:8b through Ollama, on our own computer |
| Date | 2026-10-09 10:19:10 UTC |
| End of run | the model gave a final answer |
| Time | 209 s |
| Requests to the model | 5 |
| Tokensunits of text that the model read and wrote | 35397 input, 516 output, 0 cache read, 0 cache write |
| Cost estimate | none: the model runs on our own computer |
| Tool calls | 3 (9 failed) |
| Adapters | scikit-image 0.1.1, program 0.26.0 |
| Session | 20261009-051909-16c3 |
Code hash of each step (2)
| Step | Tool | Program version | Code hash |
|---|---|---|---|
| n1 | inspect_image | 0.26.0 | cd246d8739be |
| n2 | count_objects | 0.26.0 | a909f01092a4 |
The code hash is a fingerprint of the adapter name, the adapter version, the tool and its definition in the adapter. If one of these changes, the hash changes.