Repository: mazurowski-lab/segment-anything-medical-evaluation
Branch: main
Commit: 2775b73f3165
Files: 13
Total size: 78.2 KB
Directory structure:
gitextract_s8s34ac6/
├── .gitignore
├── CITATION.md
├── LICENSE
├── README.md
├── experimental_results_tables/
│ ├── Fig2-Performance of SAM for 5 modes of Use.csv
│ ├── fig34-Table_for_focalclick_point_number_changes.csv
│ ├── fig34-Table_for_ritm_point_number_changes.csv
│ ├── fig34-Table_for_sam_oracle_point_number_changes.csv
│ ├── fig34-Table_for_sam_point_number_changes.csv
│ ├── fig34-Table_for_simpleclick_point_number_changes.csv
│ └── fig4-Table_average_overalldatasets_point_numer_changes.csv
├── prompt_gen_and_exec_v1.py
└── prompt_gen_and_exec_v2_allmode.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
*.pth
*.py.swp
run_record.py
scores*/*
.ipynb*/*
log.txt
results/*
segment-anything/*
segment_anything/*
ritm_interactive_segmentation/*
weights/*
================================================
FILE: CITATION.md
================================================
```bib
@article{mazurowski2023segment,
title={Segment anything model for medical image analysis: an experimental study},
author={Mazurowski, Maciej A and Dong, Haoyu and Gu, Hanxue and Yang, Jichen and Konz, Nicholas and Zhang, Yixin},
journal={Medical Image Analysis},
volume={89},
pages={102918},
year={2023},
publisher={Elsevier}
}
```
================================================
FILE: LICENSE
================================================
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FILE: README.md
================================================
# Segment Anything Model for Medical Image Analysis: an Experimental Study
[](https://arxiv.org/abs/2304.10517)
#### By [Maciej Mazurowski](https://sites.duke.edu/mazurowski/), Haoyu Dong, Hanxue Gu, Jichen Yang, [Nicholas Konz](https://nickk124.github.io/) and Yixin Zhang.
This is the official repository for our paper: [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://www.sciencedirect.com/science/article/pii/S1361841523001780), recently published in Medical Image Analysis, where we evaluated Meta AI's Segment Anything Model (SAM) on many medical imaging datasets.
## Installation
The code requires installing SAM's repository [Segment Anything (SAM)](https://github.com/facebookresearch/segment-anything.git). The model and dependencies can be found at SAM's repository, or you can install them with
```
git clone https://github.com/facebookresearch/segment-anything.git
cd segment-anything; pip install -e .
```
Optionally, we have included code to evaluate [Reviving Iterative Training with Mask Guidance for Interactive Segmentation (RITM)](https://arxiv.org/abs/2102.06583) on the datasets. All you need to do to use our code for this is to clone their repository locally:
```
git clone https://github.com/yzluka/ritm_interactive_segmentation
```
## Getting start
First, download SAM's model checkpoint
```
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
```
If you want to run SAM (and competing methods) with iterative prompts, run the code with:
```
python3 prompt_gen_and_exec_v1.py --num-prompt XXX --model sam/ritm
```
where it will ask you to enter the dataset you wish to evaluate on.
Optionally, to run RITM, you need to download its weights via:
```
wget https://github.com/saic-vul/ritm_interactive_segmentation/releases/download/v1.0/coco_lvis_h32_itermask.pth
```
If you want to run SAM with the 5 mode proposed in the paper, run the code with:
```
python3 prompt_gen_and_exec_v2_allmode.py
```
The 5 mode strategy includes (also shown in Figure 1, [](https://arxiv.org/abs/2304.10517)):
- 1 point at the center of the **largest** component
- 1 point at the center of **each** component (put at most 3 points)
- 1 box sharply around the **largest** component
- 1 box sharply around **each** component (put at most 3 boxes)
- 1 box covers **all** object
## Obtaining datasets from our paper
TODO
## Adding custom datasets
To evaluate your own dataset, you need to format the dataset as:
```
XXX:
images:
abc.png
def.png
...
masks:
abc.png
def.png
...
```
where images and masks should have the same name.
## News
- 1 We have released our experimental results with detailed numerical numbers that were used to make figures in our paper; these tables are under the subfolder /experimental_results_tables.
## Citation
If you find our work to be useful for your research, please cite our paper:
```
@article{mazurowski2023segment,
title={Segment anything model for medical image analysis: an experimental study},
author={Mazurowski, Maciej A and Dong, Haoyu and Gu, Hanxue and Yang, Jichen and Konz, Nicholas and Zhang, Yixin},
journal={Medical Image Analysis},
volume={89},
pages={102918},
year={2023},
publisher={Elsevier}
}
```
================================================
FILE: experimental_results_tables/Fig2-Performance of SAM for 5 modes of Use.csv
================================================
,Dataset_names,Mode 1: 1 point at largest object region,Mode 1 (oracle): 1 point at largest object region,Mode 2: 1 point at each object region,Mode 2 (oracle): 1 point at each object region,Mode 3: 1 box at largest object region,Mode 3 (oracle): 1 box at largest object region,Mode 4: 1 box at each object region,Mode 4 (oracle): 1 box at each object region,Mode 5: 1 box cover all objects,Mode 5 (oracle): 1 box cover all objects
0,CT-Organ: Lung,0.5040375609102761,0.7649961536318798,0.8399531292817298,0.8544587010504898,0.5619888040977155,0.5751254678667818,0.9118037681853222,0.9287611757965885,0.8442930189948117,0.9014606792388544
1,MRI-Spine: SP,0.7073228746669389,0.7096432977416823,0.7070476025992274,0.7095302007531917,0.9043487094339112,0.9119653011188062,0.9043487094339112,0.9119653011188062,0.9043487094339112,0.9119653011188062
2,CT-Spleen,0.8691847407254183,0.8772478846904421,0.8689422709715835,0.8768474552752175,0.8888784234966336,0.8960502031503226,0.8903440667420405,0.8977486661086032,0.888157727658361,0.896161751204274
3,Xray-Chest,0.488912549431233,0.7042973771124544,0.8255508338566884,0.8304664474345188,0.4845595757679478,0.5063367845139323,0.8808300821393182,0.9088518824912722,0.8380273852364888,0.8731804644274592
4,Xray-Hip: Ilium,0.8649546268769193,0.8775119614476239,0.8649960616662165,0.8776958070689208,0.8671305346236142,0.9498623353564899,0.8671305346236142,0.9498623353564899,0.8671305346236142,0.9498623353564899
5,CT-Liver,0.7168836341782948,0.7247227437045324,0.6897820158324605,0.6957833671590816,0.8339470767285759,0.8497763577430864,0.8454871317234407,0.8613084541832972,0.8244972081253991,0.841507070072933
6,CT-Organ: Kidney,0.48812141016765004,0.5137771486899364,0.5293984833999216,0.5306062550294209,0.528200113701847,0.53434510978159,0.8309711867869959,0.8391476064107741,0.45035835954319947,0.5159415625931333
7,CT-Organ: Bladder,0.5916728074734098,0.5916739116938898,0.5726033235071356,0.5726221823458707,0.7796024605322711,0.8196618408524342,0.7868766417826404,0.8290201142372928,0.7608618606538163,0.7958953774032693
8,Xray-Hip: Femur,0.6737930817054792,0.6845047915027754,0.6740167144857426,0.6847395375238928,0.7867385105826606,0.8058534761556695,0.7867375836188909,0.8058525491918997,0.7867528936815141,0.805867859254523
9,MRI-Heart,0.573143704527502,0.583662130445331,0.5775095522204375,0.5881684497095361,0.734538458869586,0.7669220221895826,0.7856222042741158,0.8176279421491547,0.6293998738464587,0.7048879357807536
10,CT-Organ: Liver,0.6572655994639135,0.657509567934053,0.6356032466211343,0.6358335035559992,0.7789995383252979,0.8760939976763359,0.7854910304887937,0.8823629020543077,0.7778099650265168,0.8718598005716134
11,US-Kidney,0.5531198274177759,0.6843526106476006,0.5569639972326419,0.6782384069681024,0.7717521567805573,0.8660795122175549,0.7722598559812998,0.8665291114759625,0.771622575672267,0.8660049668283785
12,CT-Colon,0.5241501535721865,0.5307969474576953,0.5250511629078818,0.5313687214826,0.6987196146385244,0.7210953366255343,0.7180784774651142,0.7408698698258476,0.6941207593677051,0.7208699936931542
13,CT-Organ: Bone,0.3870924726703019,0.537948309577858,0.5617453967171664,0.6075720277255027,0.5041121728098235,0.5591699598152509,0.7179510572142475,0.7861382489199804,0.4318603175075531,0.555966121641184
14,CT-Pancreas,0.5010115151433743,0.532924268069487,0.4980692434242409,0.5275509005159461,0.6761699849486295,0.7309784379842976,0.6952301940549994,0.7507288043045295,0.656463026747013,0.7209926850758989
15,MRI-Prostate,0.4716223475002687,0.5064347886055016,0.47104135778245915,0.5058923133864082,0.6843254450929145,0.7592649707750968,0.6848254784853673,0.7597124063727306,0.6834499338952817,0.7585286565598245
16,US-Breast,0.4712195546907309,0.6188008177558781,0.4735816855561947,0.6155673166376364,0.6410504608959803,0.7790617068901438,0.6410492376798388,0.7790555125298435,0.641132505088589,0.7790603773140136
17,MRI-Breast: Breast,0.34783472864351483,0.49746769921502504,0.35509528978447896,0.5012692482207185,0.5899735090916093,0.8347561389801192,0.6060022011834233,0.8527033667723514,0.5937453747396176,0.8437996657670627
18,PET-Wholebody,0.3426932634019096,0.3520103866842598,0.326432439783384,0.3328435709649819,0.5173322095044326,0.6173761929957494,0.562570259343904,0.6693695039761657,0.4643212895824232,0.5706536068818072
19,CT-Hepaticvessel,0.24518548187187356,0.24979626329976973,0.15671143905373192,0.1588021329729012,0.41687928725650014,0.46549963889514995,0.5419016843275795,0.5905769848091643,0.22333774646603485,0.25288755765451426
20,MRI-Brain: GD,0.3644789966578506,0.3729348517929812,0.3510795231936171,0.35839340193525926,0.4884324398981961,0.5417414636069973,0.5092892536422939,0.5646283585515393,0.47862140678629467,0.5270173818782171
21,MRI-Breast: FGT,0.2891343803228075,0.2985958075634497,0.22236704406413602,0.22236704406413602,0.34043336460000645,0.3467864983150635,0.49118344693603677,0.4944861199774202,0.2275343719139432,0.2550043716169522
22,US-Variantumor,0.3398076921862697,0.4573707014742938,0.34383180057461576,0.459333444052403,0.4633740285070635,0.7190842974900171,0.46523688156504733,0.7211977909933263,0.4639737355438073,0.7202034744723546
23,MRI-Brain: Core,0.2586909570158529,0.26170807541206154,0.23217271635685718,0.23415282948636373,0.416183998958719,0.45198666074925037,0.45756469370085256,0.5012820015283274,0.3333978894984103,0.35959147741753417
24,MRI-Brain: Edema,0.26076432589689647,0.26263997215298457,0.23422116339198454,0.23653493691978156,0.41239031745961985,0.46911298689380737,0.4501623828483823,0.5098458542001842,0.3605293097304758,0.41228754855160304
25,MRI-Spine: GM,0.11364871552061874,0.11364871552061874,0.11363235284637922,0.11363235284637922,0.2783918412038833,0.2877485790893153,0.2814359955162776,0.2910305845120911,0.2782344908831689,0.2874760097423331
26,US-Nerve,0.12620618960020696,0.14699303717568177,0.12688055807197968,0.1475196349207403,0.23287614303247803,0.543738210855488,0.23287614303247803,0.543738210855488,0.23287614303247803,0.543738210855488
27,US-Muscle,0.17728576808683244,0.3060046121522509,0.17914924775826055,0.30700876202985417,0.213416606828824,0.7724017740374894,0.2134182856841066,0.7724058881872616,0.21340544435737385,0.772385439486003
================================================
FILE: experimental_results_tables/fig34-Table_for_focalclick_point_number_changes.csv
================================================
Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Breast: Breast,MRI-Breast: FGT,Xray-Chest,Xray-Hip: Ilium,Xray-Hip: Femur,US-Breast,US-Kidney,US-Nerve,US-Muscle,US-Variantumor,CT-Liver,CT-Organ: Liver,CT-Organ: Bladder,CT-Organ: Lung,CT-Organ: Kidney,CT-Organ: Bone,CT-Spleen,CT-Colon,CT-Pancreas,CT-Hepatovessel,PET-Wholebody
1,0.004025254,0.027928349,0.113857237,0.175645957,0.03986095,0.075069539,0.070572936,0.457802043,0.1895125,0.427554391,0.708503111,0.475881822,0.54081021,0.504198998,0.294929264,0.253375321,0.293808738,0.123678439,0.133033298,0.144042378,0.295752141,0.054716302,0.164837266,0.501527525,0.107550383,0.035529687,0.044232954,0.014385373
2,0.037371442,0.235181014,0.321559345,0.381946207,0.13743478,0.193391269,0.180658076,0.63880211,0.251257308,0.593945688,0.644230198,0.517541668,0.666885296,0.650718912,0.51178986,0.462183475,0.538999815,0.406077525,0.446821573,0.420565875,0.482557875,0.315196521,0.390300968,0.676238626,0.252521424,0.209702186,0.096300469,0.062876195
3,0.052412104,0.309360884,0.441770545,0.565831141,0.170330246,0.307568209,0.259887872,0.757668926,0.312243651,0.734368014,0.646724595,0.474139465,0.741157111,0.730287091,0.613760204,0.521225309,0.688531831,0.623366324,0.580700973,0.498732137,0.537030405,0.453235187,0.45147101,0.751979752,0.328490041,0.312865563,0.162824101,0.105695172
4,0.069027901,0.364489127,0.509469233,0.666893419,0.178108232,0.349356889,0.281664208,0.82831261,0.343710686,0.826458024,0.647969761,0.477270964,0.785818353,0.79611497,0.687293409,0.588059884,0.758672241,0.722269748,0.618459182,0.512337014,0.610735233,0.594804091,0.508565103,0.785288622,0.381492659,0.378995336,0.211513325,0.134701685
5,0.095872792,0.420259525,0.555479043,0.724153243,0.180289088,0.367112475,0.286844621,0.852578554,0.356086718,0.861720631,0.652154379,0.465022331,0.814209694,0.827644195,0.738322571,0.679083091,0.811598345,0.777060986,0.647416519,0.522419772,0.61735451,0.633075598,0.541030351,0.809464194,0.423001174,0.433595818,0.242501905,0.164536063
6,0.093518396,0.484263019,0.587125317,0.756102812,0.181137974,0.378856699,0.289923621,0.87569604,0.36313737,0.882275747,0.652971097,0.465608305,0.841888733,0.84342565,0.772921105,0.741472471,0.842666603,0.811999407,0.665484014,0.530768205,0.640768748,0.667851201,0.571398107,0.826310776,0.459383865,0.483031031,0.263238971,0.197997842
7,0.081247272,0.537294983,0.607956103,0.783828811,0.181238216,0.388258647,0.290885048,0.889782916,0.374244758,0.897437485,0.652454306,0.46276503,0.856383756,0.852037007,0.797975349,0.78428192,0.865014988,0.833933397,0.673719344,0.537998042,0.646140145,0.688874425,0.590814707,0.838715872,0.488143128,0.527070534,0.275937206,0.217199647
8,0.074974276,0.580202232,0.625476621,0.805202033,0.181652181,0.39319481,0.290999649,0.897631706,0.380314859,0.905907584,0.648484516,0.461930857,0.869769538,0.860097299,0.817469358,0.812780729,0.882941019,0.849989042,0.681943505,0.540466828,0.646468513,0.698536529,0.607636286,0.847639387,0.50355775,0.562378446,0.284445333,0.234339204
9,0.064372595,0.617293354,0.636814772,0.81873772,0.182106247,0.396528486,0.291566884,0.905754443,0.386745736,0.910290517,0.650224492,0.461491777,0.878295597,0.86621057,0.831896302,0.835481466,0.894598737,0.860873497,0.684290566,0.544097253,0.649310387,0.705362618,0.618825187,0.857405388,0.525595827,0.592320924,0.292000151,0.237753194
================================================
FILE: experimental_results_tables/fig34-Table_for_ritm_point_number_changes.csv
================================================
Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Breast: Breast,MRI-Breast: FGT,Xray-Chest,Xray-Hip: Ilium,Xray-Hip: Femur,US-Breast,US-Kidney,US-Nerve,US-Muscle,US-Variantumor,CT-Liver,CT-Organ: Liver,CT-Organ: Bladder,CT-Organ: Lung,CT-Organ: Kidney,CT-Organ: Bone,CT-Spleen,CT-Colon,CT-Pancreas,CT-Hepatovessel,PET-Wholebody
1,0.009776697,0.064966565,0.031878882,0.056176431,0.025811666,0.136587317,0.178802495,0.401831015,0.127263521,0.017399807,0.473251112,0.247120046,0.227658544,0.186122093,0.029403263,0.210569491,0.203615625,0.086053854,0.115138964,0.337620822,0.194030643,0.084108101,0.147452457,0.036503824,0.022889909,0.016603625,0.01956532,0.014308346
2,0.068667712,0.347604107,0.249125446,0.1944822,0.210006062,0.282736389,0.264742438,0.546480434,0.207528461,0.189691616,0.646843857,0.24760928,0.533022288,0.241617361,0.162263621,0.300972757,0.485504215,0.382499465,0.456108544,0.514644855,0.323987645,0.222086953,0.271730279,0.476155671,0.241041065,0.148854411,0.09798397,0.182438889
3,0.154209721,0.746773816,0.55001828,0.340019777,0.236681708,0.361771356,0.309778051,0.699137796,0.251447991,0.477084273,0.688482455,0.247425019,0.663302256,0.44918303,0.27393084,0.39539574,0.600270934,0.618278285,0.543890146,0.577171719,0.42489841,0.311404133,0.369327315,0.806914013,0.489541566,0.462619064,0.279317908,0.378661778
4,0.21684836,0.892405101,0.676870639,0.501080067,0.242542209,0.394699765,0.319229324,0.804759199,0.28326333,0.699922214,0.69362893,0.247221362,0.750896218,0.713147694,0.359931088,0.445786427,0.704843719,0.738720174,0.592463057,0.598386179,0.480453971,0.372974286,0.445158454,0.887687304,0.60397035,0.613247552,0.368131341,0.469181529
5,0.265030949,0.924699564,0.744049431,0.629485554,0.242154976,0.412765858,0.321642999,0.865720902,0.337969453,0.809688804,0.695911906,0.247417335,0.810460731,0.811335838,0.428248214,0.643546062,0.776301034,0.809028787,0.623963797,0.610775455,0.49233884,0.435245423,0.49780431,0.909903533,0.691907605,0.704259619,0.427880846,0.523284619
6,0.293561637,0.936065097,0.781527363,0.730566168,0.241348761,0.425597662,0.322459763,0.898664187,0.396409222,0.864812026,0.696607856,0.247236398,0.8438083,0.862407998,0.535131673,0.707554486,0.827314184,0.857053478,0.642652954,0.619250626,0.498056709,0.475407592,0.521437292,0.923824123,0.753749049,0.768801437,0.470085891,0.553297118
7,0.339553131,0.941408456,0.809197888,0.798286357,0.239756967,0.432402415,0.322503837,0.91438182,0.419718229,0.891209937,0.69633196,0.247038081,0.872005705,0.900013577,0.635584238,0.754948718,0.866732198,0.887371351,0.650995682,0.625320391,0.505517236,0.500040239,0.531951126,0.932297287,0.788843536,0.809973718,0.502399452,0.56522908
8,0.397066238,0.94591996,0.82966044,0.839945735,0.239260708,0.437825163,0.321764581,0.927722872,0.43637957,0.905989902,0.696155826,0.246879107,0.893606449,0.92246739,0.72420605,0.786267115,0.89277063,0.907604086,0.658782923,0.629542152,0.508517805,0.515757562,0.539467557,0.938039262,0.817118128,0.838108995,0.52857919,0.581858984
9,0.412193052,0.949736334,0.845647817,0.868920434,0.238503004,0.441827948,0.32143899,0.93506419,0.456128524,0.917510153,0.695700578,0.246786427,0.908231648,0.932989257,0.77823478,0.813730829,0.909237121,0.920241409,0.663375988,0.63332767,0.510802672,0.528933515,0.545852604,0.941726217,0.83594024,0.857004672,0.546440041,0.594484696
================================================
FILE: experimental_results_tables/fig34-Table_for_sam_oracle_point_number_changes.csv
================================================
Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Breast: Breast,MRI-Breast: FGT,Xray-Chest,Xray-Hip: Ilium,Xray-Hip: Femur,US-Breast,US-Kidney,US-Nerve,US-Muscle,US-Variantumor,CT-Liver,CT-Organ: Liver,CT-Organ: Bladder,CT-Organ: Lung,CT-Organ: Kidney,CT-Organ: Bone,CT-Spleen,CT-Colon,CT-Pancreas,CT-Hepatovessel,PET-Wholebody
1,0.113382997,0.709539584,0.584178611,0.505804327,0.262528162,0.263294527,0.372029612,0.345463166,0.281663085,0.703325319,0.877513169,0.684958183,0.619357476,0.683999403,0.146854203,0.306368149,0.457226535,0.724377986,0.657583236,0.591689781,0.756044629,0.500723179,0.537987563,0.877240458,0.530114986,0.532048976,0.247168962,0.35194111
2,0.119773235,0.744939523,0.623545727,0.572477487,0.260994068,0.285302196,0.381234603,0.6758047,0.282176727,0.757258261,0.866834801,0.693362246,0.662810424,0.724950099,0.21833731,0.299017412,0.505241469,0.763792433,0.747898034,0.651779335,0.777408925,0.541486882,0.647947162,0.884336822,0.576671729,0.584240611,0.261465026,0.373038835
3,0.131098987,0.77523773,0.661308732,0.628740032,0.259306542,0.307602154,0.385021197,0.739919723,0.346261046,0.805092075,0.842825267,0.685079064,0.688989909,0.737467933,0.316962782,0.336051904,0.547289692,0.805481587,0.784161253,0.693749368,0.78040897,0.580062795,0.639216857,0.889431374,0.615054616,0.632621648,0.269766316,0.397435305
4,0.144442728,0.807678556,0.692461091,0.667809023,0.261313032,0.32947915,0.389469012,0.768061551,0.392392058,0.829856068,0.822066657,0.687697527,0.701730236,0.744358425,0.385976748,0.374349785,0.583980264,0.824626046,0.806545088,0.711553102,0.793423502,0.564309555,0.648418237,0.890618712,0.639570084,0.671549271,0.299500696,0.426413642
5,0.161924661,0.845353502,0.712152915,0.699246907,0.265616183,0.341519375,0.391408173,0.777935351,0.407752693,0.83866496,0.811284519,0.677701683,0.724727862,0.75665265,0.437000192,0.403446497,0.62484449,0.830443839,0.812466614,0.724030866,0.82727534,0.551586181,0.653927076,0.890058509,0.658637482,0.698534343,0.324923983,0.455377389
6,0.167757436,0.864340548,0.723892649,0.71829192,0.269502834,0.344262467,0.392093148,0.793504565,0.422228556,0.854127414,0.804561141,0.675640243,0.724652364,0.769091511,0.47065314,0.416911855,0.652996022,0.839153688,0.816542629,0.728040748,0.837140309,0.551943499,0.660250726,0.893487344,0.672777098,0.716694442,0.343314906,0.473515701
7,0.173125413,0.874823174,0.732049981,0.736758716,0.272990221,0.344493113,0.390468607,0.803812689,0.436776109,0.854147837,0.792230632,0.67081475,0.735734646,0.775930539,0.493510469,0.422737161,0.673982636,0.845186259,0.814895267,0.731619981,0.836369617,0.542190014,0.664648044,0.896358762,0.683805082,0.731251292,0.355798264,0.489787023
8,0.183577247,0.88637115,0.741975592,0.746546349,0.275315608,0.342568224,0.387580426,0.803870108,0.458241272,0.854426157,0.778807153,0.661918896,0.7418308,0.783291581,0.5123567,0.421774649,0.687583437,0.851054856,0.812642294,0.732826391,0.835499331,0.544930103,0.667525656,0.897994785,0.697964294,0.740121265,0.364902054,0.502679934
9,0.190396554,0.897643678,0.753222393,0.755160615,0.276963077,0.340643781,0.383814678,0.787328514,0.460068245,0.854899836,0.769445566,0.655260741,0.745393265,0.784851411,0.526184921,0.416789598,0.698905152,0.85512019,0.812771086,0.732771328,0.833912249,0.549772844,0.669674454,0.900418815,0.701900522,0.74757714,0.3734387,0.511993308
================================================
FILE: experimental_results_tables/fig34-Table_for_sam_point_number_changes.csv
================================================
Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Breast: Breast,MRI-Breast: FGT,Xray-Chest,Xray-Hip: Ilium,Xray-Hip: Femur,US-Breast,US-Kidney,US-Nerve,US-Muscle,US-Variantumor,CT-Liver,CT-Organ: Liver,CT-Organ: Bladder,CT-Organ: Lung,CT-Organ: Kidney,CT-Organ: Bone,CT-Spleen,CT-Colon,CT-Pancreas,CT-Hepatovessel,PET-Wholebody
1,0.113413977,0.707215114,0.573544732,0.471484832,0.259309921,0.261415985,0.363609559,0.345463166,0.281663085,0.488023392,0.864998503,0.674170642,0.469474787,0.554015436,0.12686366,0.178319396,0.339103869,0.716429507,0.657342781,0.591651548,0.489052597,0.475591875,0.387103928,0.869169392,0.52332346,0.500065368,0.242615495,0.342118366
2,0.119720765,0.741655333,0.613681693,0.554351424,0.259478487,0.26830508,0.370015985,0.6758047,0.282176727,0.815230861,0.866504315,0.682316191,0.556444339,0.655622439,0.194500321,0.226703197,0.457742115,0.75970764,0.748821195,0.651586276,0.836874513,0.53963711,0.590489785,0.874130197,0.568189091,0.571431809,0.259368123,0.367411142
3,0.130625675,0.775255575,0.641411695,0.612059803,0.25741975,0.276364913,0.369346785,0.739919723,0.346261046,0.842280125,0.82674459,0.657379542,0.600844455,0.679694542,0.297765281,0.262841288,0.509721222,0.794624795,0.779262286,0.676877934,0.844412218,0.577549233,0.637491155,0.865739229,0.595874492,0.618693877,0.267563546,0.388570816
4,0.140929047,0.808272794,0.66301319,0.654204918,0.258600237,0.281275707,0.362243969,0.768061551,0.392392058,0.843979369,0.823342113,0.671320349,0.621606089,0.677511842,0.364005482,0.322279245,0.557451932,0.809056451,0.795562604,0.703767267,0.810856815,0.559227768,0.661925752,0.840927741,0.612444449,0.65265008,0.296264734,0.41660268
5,0.157720416,0.843728096,0.646885278,0.686865409,0.261668629,0.28317044,0.356432685,0.777935351,0.407752693,0.849559747,0.806677077,0.694991678,0.616813718,0.661007505,0.407601655,0.408274878,0.600053731,0.819339927,0.804950482,0.712652346,0.799093396,0.539457276,0.665058131,0.84107665,0.625167238,0.676747083,0.321142828,0.441237441
6,0.165206027,0.858033034,0.663534131,0.700802231,0.264560409,0.280564741,0.348235591,0.793504565,0.422228556,0.846243434,0.791316816,0.685539102,0.614913468,0.636224163,0.438131547,0.469440695,0.613285541,0.824457076,0.808271998,0.716800062,0.803959442,0.532942121,0.671274585,0.857472527,0.63745082,0.691322993,0.339571283,0.462745665
7,0.170710257,0.877168916,0.672949965,0.718864864,0.266768938,0.27679308,0.339407058,0.803812689,0.436776109,0.838384875,0.765968568,0.672491214,0.607713174,0.628649917,0.455333945,0.50208456,0.629049662,0.833091981,0.807588849,0.719530307,0.794449968,0.51318959,0.674749771,0.86698824,0.653521569,0.707366518,0.35531408,0.476078195
8,0.179191533,0.88327955,0.703467031,0.72961057,0.267926433,0.271807464,0.332516189,0.803870108,0.458241272,0.830292781,0.748915342,0.645784546,0.600356357,0.615247279,0.470843702,0.501249768,0.640108247,0.84212085,0.802128089,0.722922894,0.781299554,0.497305569,0.675521227,0.875795185,0.669449242,0.716551435,0.366660119,0.489572866
9,0.184767742,0.895182706,0.717627308,0.735748131,0.268970293,0.268623994,0.326253536,0.787328514,0.460068245,0.82685649,0.74433395,0.63887511,0.5971176,0.60587999,0.482580404,0.478826268,0.640657611,0.848753094,0.803524566,0.718247447,0.754021906,0.496107277,0.677250297,0.884291823,0.670955895,0.725567878,0.375163374,0.501396368
================================================
FILE: experimental_results_tables/fig34-Table_for_simpleclick_point_number_changes.csv
================================================
Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Breast: Breast,MRI-Breast: FGT,Xray-Chest,Xray-Hip: Ilium,Xray-Hip: Femur,US-Breast,US-Kidney,US-Nerve,US-Muscle,US-Variantumor,CT-Liver,CT-Organ: Liver,CT-Organ: Bladder,CT-Organ: Lung,CT-Organ: Kidney,CT-Organ: Bone,CT-Spleen,CT-Colon,CT-Pancreas,CT-Hepatovessel,PET-Wholebody
1,0.036917364,0.24013777,0.32813968,0.156812103,0.070737675,0.073833558,0.074938771,0.384828897,0.120320148,0.301886825,0.338930018,0.139755433,0.411324625,0.18485997,0.033587518,0.208382876,0.195995699,0.100781133,0.076410721,0.029517988,0.433976494,0.236869678,0.295704409,0.160617213,0.24045458,0.306115354,0.083992175,0.083024798
2,0.074315899,0.438909908,0.509669764,0.345343094,0.176221411,0.233113111,0.245429407,0.560587466,0.17447836,0.759687667,0.558547507,0.150257849,0.5822415,0.376086352,0.167600733,0.212095385,0.448687145,0.439668434,0.327814714,0.187063209,0.557230434,0.273434594,0.460289411,0.372857387,0.370298809,0.445682783,0.145724064,0.161255947
3,0.111411227,0.601302105,0.623511013,0.515320271,0.204729417,0.353292809,0.32309356,0.690514998,0.251615944,0.80702532,0.610816165,0.159405573,0.706460364,0.54632359,0.336670784,0.386810195,0.642558025,0.633103056,0.448854849,0.390500837,0.682851036,0.527287338,0.540123064,0.641800154,0.497363944,0.576087301,0.236427216,0.254708616
4,0.145873285,0.704035775,0.67408799,0.627307873,0.212863807,0.39269438,0.340823454,0.783280141,0.318457856,0.870576472,0.608568184,0.163149144,0.778123062,0.696258158,0.506423285,0.46089373,0.75090271,0.728375573,0.496894148,0.464911241,0.729745385,0.650564222,0.576397223,0.78523993,0.574217989,0.653446876,0.299489016,0.319963864
5,0.178057912,0.769424843,0.705193028,0.70631438,0.215447934,0.414631131,0.346224984,0.849528716,0.356345058,0.898726587,0.608904009,0.1635142,0.82435243,0.794294994,0.633361621,0.55701521,0.824221983,0.788878818,0.533639099,0.50066737,0.752613232,0.707905958,0.599769217,0.847601037,0.629973033,0.703643714,0.34292095,0.383902662
6,0.203528154,0.811252956,0.734680222,0.758216841,0.216681591,0.42858391,0.348861456,0.880011884,0.39786577,0.916010945,0.610065063,0.163581144,0.86052427,0.844360641,0.703675,0.638759001,0.860300985,0.829969299,0.561197229,0.514743443,0.756157771,0.735048871,0.619552816,0.881396382,0.672322956,0.742477014,0.376187727,0.44142806
7,0.225104011,0.838638255,0.755109759,0.796218067,0.217525453,0.439049634,0.351082259,0.901187755,0.423384017,0.924646584,0.610104041,0.163646219,0.879463806,0.871914095,0.752700252,0.699594984,0.886310947,0.856232022,0.575102865,0.523347094,0.755540253,0.751679337,0.636610438,0.895181059,0.706988799,0.770656071,0.401466605,0.483005371
8,0.249185049,0.862994056,0.773623544,0.820442908,0.217706336,0.44749058,0.352220971,0.915576452,0.434242601,0.929429077,0.610209747,0.163664286,0.892879742,0.892682051,0.788452519,0.748429821,0.902504666,0.875388962,0.584143075,0.528106312,0.758542131,0.766925149,0.653760277,0.90289883,0.727813526,0.791685955,0.420597816,0.519655867
9,0.262713832,0.880448639,0.787876751,0.842244168,0.217946907,0.454003211,0.352453584,0.92839559,0.447385722,0.935918749,0.611428802,0.163666056,0.904899678,0.905523481,0.816563445,0.785038715,0.914627466,0.88973461,0.590006894,0.532383632,0.755828556,0.777167205,0.664510904,0.912021661,0.74913438,0.809574149,0.438426564,0.552590644
================================================
FILE: experimental_results_tables/fig4-Table_average_overalldatasets_point_numer_changes.csv
================================================
Num of points,SAM,SAM (oracle),RITM,SimpleClick,FocalClick
1,0.459519799,0.508014549,0.132232515,0.191030481,0.224022227
2,0.539567888,0.553004503,0.303443928,0.348378298,0.382966275
3,0.566878414,0.581487316,0.453819192,0.474998885,0.469059209
4,0.584634865,0.602130352,0.539908923,0.546913028,0.52206614
5,0.596895064,0.618017651,0.59617223,0.594181218,0.553567435
6,0.604929737,0.628477461,0.63195318,0.625265764,0.577543683
7,0.609457031,0.634867725,0.656464736,0.646124645,0.593629751
8,0.611501257,0.63986344,0.675259442,0.661830439,0.605229646
9,0.611249208,0.642368666,0.687500386,0.6743755,0.614151596
================================================
FILE: prompt_gen_and_exec_v1.py
================================================
from segment_anything import SamPredictor, sam_model_registry
from PIL import Image, ImageDraw, ImageOps
from shapely.geometry import LineString, MultiLineString, Polygon, Point, GeometryCollection
from skimage.morphology import medial_axis
from scipy.optimize import minimize_scalar
from scipy.ndimage import binary_dilation
from skimage.measure import label
from sklearn.cluster import KMeans
import argparse
import os
import cv2
import json
import imutils
import random
import matplotlib.pyplot as plt
import numpy as np
# Fix randomness in prompt selection
np.random.seed(1)
import sys
sys.path.append('FocalClick')
#sys.path.append('ritm_interactive_segmentation')
#sys.path.append('CFR-ICL-Interactive-Segmentation')
from isegm.inference.clicker import Click
from isegm.inference import utils as is_utils
from isegm.inference.predictors import get_predictor as is_get_predictor
from isegm.inference.evaluation import evaluate_sample_onepass as is_evaluate_sample_onepass
#This is a helper function that should not be called directly
def _find_closest(centroid, pos_points):
dist_squared = np.sum((pos_points - centroid)**2, axis=1)
point_idx = np.argmin(dist_squared)
return pos_points[point_idx]
def IOU(pm, gt):
a = np.sum(np.bitwise_and(pm, gt))
b = np.sum(pm) + np.sum(gt) - a #+ 1e-8
if b == 0:
return -1
else:
return a / b
def IOUMulti(y_pred, y):
score = 0
numLabels = np.max(y)
if np.max(y) == 1:
score = IOU(y_pred, y)
return score
else:
count = 1
for index in range(1,numLabels+1):
curr_score = IOU(y_pred[y==index], y[y==index])
print(index, curr_score)
if curr_score != -1:
score += curr_score
count += 1
return score / (count - 1) # taking average
####################################################
# input: raw_msk
# A mask should containing no 'void' class.
# Binary mask should have value {0,1} but not {0,255}
# output:
# A list of region profiles; Each profile takes the form
# {'loc':[x0,y0,x1,y1], 'cls': cls}
# 'loc' is a list with 4 elements ; 'cls' is object class as integer
####################################################
def MaskToBoxes(mask):
label_msk, region_ids = label(mask, connectivity=2, return_num=True)
bbox_profiles = []
for region_id in range(1, region_ids+1):
#find coordinates of points in the region
row,col = np.argwhere(label_msk == region_id).T
#find class of the region
cls = mask[row[0],col[0]]
# find the four corner coordinates
y0,x0 = row.min(),col.min()
y1,x1 = row.max(),col.max()
bbox_profiles.append({'loc':[x0,y0,x1,y1], 'cls':cls})
return bbox_profiles
####################################################
# input: raw_msk
# A mask should containing no 'void' class.
# Binary mask should have value {0,1} but not {0,255}
# input: N
# The number of points to apply on each object/connected region
# output:
# A list of region profiles. Each region profile takes the form
# {'loc':np.array([[x0,y0],[x1,y1],[x_N,y_N]]), 'cls': cls}
# 'loc' is 2D array with shape (N, 2); 'cls' is object class as integer
####################################################
def Mask2Points(raw_msk, N=1):
label_msk, region_ids = label(raw_msk, connectivity=2,return_num = True)
point_profiles = []
for region_id in range(1, region_ids+1):
#find coordinates of points in the region
pos_points = np.argwhere(label_msk == region_id)
# clean some region that is abnormally small
r = len(pos_points) / len(raw_msk.flatten())
if r < 1e-4:
continue
print('mask ratio', r)
#if len(pos_points) < len(raw_msk.flatten())*0.001:
# continue
#get the skeleton
binary_msk = np.where(label_msk == region_id,1,0)
skeleton_msk = medial_axis(binary_msk).astype(np.uint8)
skeleton_points = np.argwhere(skeleton_msk>0)
# Cluster and assign the object skeleton into N sections
#kmean = KMeans(n_clusters=N,n_init=3, algorithm='lloyd' if N == 1 else 'elkan').fit(skeleton_points)
kmean = KMeans(n_clusters=N,n_init=3, algorithm='auto').fit(skeleton_points)
cluster_assigned = np.zeros(len(skeleton_points)) if N == 1 else kmean.predict(skeleton_points)
centroids = kmean.cluster_centers_
# pick a skeleton point closest to the centroid from each cluster
selected_points = np.zeros((N,2))
for cluster_id, centroid in zip(range(N),centroids):
points_in_cluster = skeleton_points[cluster_assigned==cluster_id]
selected_points[cluster_id] = _find_closest(centroid,points_in_cluster)
#find class of the region
cls = raw_msk[pos_points[0,0],pos_points[0,1]]
point_profiles.append({'loc':np.concatenate((selected_points[:,1:],selected_points[:,0:1]),axis=1), 'cls':cls})
#TODO: double check if > 1 regions found
break
return point_profiles
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="SAG segmentor for medical images")
parser.add_argument("--num-prompt", default=1, type=int, help="number of prompts to include, negative number means using box as prompts")
parser.add_argument("--class-type", default="b", type=str, help="binary or multi class, choose b or m")
parser.add_argument("--model-path", default="./", type=str, help="the path of the model saved")
parser.add_argument("--init-path", default="./", type=str, help="the path of the dataset")
parser.add_argument("--model", default="sam", type=str, help="the model to use as predictor")
parser.add_argument("--oracle", default=False, type=bool, help="whether eval in the oracle mode, where best prediction is selected based on GT")
parser.add_argument("--result-image",default="./results",type=str, help="the path to save segmented results")
parser.add_argument("--result-score",default="./scores",type=str, help="the path to save result metrics")
args = parser.parse_args()
# Set up model
if args.model == 'sam':
sam = sam_model_registry["default"](checkpoint=os.path.join(args.model_path, "sam_vit_h_4b8939.pth"))
sam.to('cuda')
predictor = SamPredictor(sam)
# NOTE: manual change sys path when importing library
elif args.model == 'ritm':
model = is_utils.load_is_model(os.path.join(args.model_path, "coco_lvis_h32_itermask.pth"), "cuda")
predictor = is_get_predictor(model, "NoBRS", "cuda")
elif args.model == 'sc':
model = is_utils.load_is_model(os.path.join(args.model_path, "cocolvis_icl_vit_huge.pth"), "cuda", eval_ritm=False)
zoom_in_params = {
'skip_clicks': -1,
'target_size': (448, 448)
}
predictor_params = {
'cascade_step': 4 + 1,
'cascade_adaptive': True,
'cascade_clicks': 1
}
predictor = is_get_predictor(model, "NoBRS", "cuda", prob_thresh=0.49, \
predictor_params=predictor_params, zoom_in_params=zoom_in_params)
elif args.model == 'fc':
model = is_utils.load_is_model(os.path.join(args.model_path, "segformerB3_S2_comb.pth"), "cuda")
predictor = is_get_predictor(model, "NoBRS", "cuda", prob_thresh=0.49)
print('Dataset you can choose among: chest, gmsc_sp, gmsc_gm, breast_b, breast_f, heart, usbreast, liver, prostate, nodule, brats, all')
# Set up dataset
dataset = input("Type of input: ")
if dataset == 'all':
dataset_list = ['busi', 'breast_b', 'breast_d', 'chest', 'gmsc_sp', 'gmsc_gm', 'heart', 'liver', 'petwhole', 'prostate', 'brats_3m', 'xrayhip', \
'ctliver', 'ctorgan', 'ctcolon', 'cthepaticvessel', 'ctpancreas', 'ctspleen', 'usmuscle', 'usnerve', 'usovariantumor']
else:
dataset_list = [dataset]
for dataset in dataset_list:
print('curr dataset', dataset)
num_class = 1
if 'gmsc' in dataset:
input_img_dir = os.path.join(args.init_path, 'sa_gmsc/images')
input_seg_dir = os.path.join(args.init_path, 'sa_gmsc/masks')
elif 'breast' in dataset:
input_img_dir = os.path.join(args.init_path, "sa_dbc-2D/imgs")
if dataset == 'breast_b':
input_seg_dir = os.path.join(args.init_path, "sa_dbc-2D/masks_breast")
else:
input_seg_dir = os.path.join(args.init_path, "sa_dbc-2D/masks_dense-tissue")
else:
input_img_dir = os.path.join(args.init_path, 'sa_%s/images' % dataset)
input_seg_dir = os.path.join(args.init_path, 'sa_%s/masks' % dataset)
if dataset == 'brats_3m':
num_class = 3
if dataset == 'xrayhip':
num_class = 2
if dataset == 'ctorgan':
num_class = 5
# target is a variable only used by GMSC
if dataset == 'gmsc_sp':
target = 'sp'
if dataset == 'gmsc_gm':
target = 'gm'
print(input_img_dir)
print(input_seg_dir)
if args.num_prompt<0:
save_path = os.path.join('results',dataset,'box')
elif args.oracle:
save_path = os.path.join('results',dataset,'oracle')
else:
save_path = os.path.join('results',dataset,'point')
# Running
dc_log, names = [], []
mask_list = os.listdir(input_seg_dir)
print('# of dataset', len(mask_list))
# VIS: now VIS function is separted into another file. Only provide mask if needed
vis = False
# Change to [name1, name2, ...] if only need to run on a few samples
im_list = None#['CHNCXR_0061_0_mask.png']
for im_idx, im_name in enumerate(mask_list):
# Skip non-selected images if specified
print(im_name)
if im_list is not None:
if im_name not in im_list:
continue
# GMSC: All masks in the same dir, separated by names
if 'gmsc' in dataset:
if target not in im_name:
continue
if 'DS_Store' in im_name:
continue
# Read image and mask
try:
input_mask = cv2.imread(os.path.join(input_seg_dir, im_name), 0)
except:
print('Cannot read mask', im_name)
continue
if np.max(input_mask) == 0:
print('Empty mask')
print('*****')
continue
# In multi-class setting, we assume classes are labeled 0,1,2,3...
# BraTS has label 1,2,4
if 'brats' in dataset:
input_mask[input_mask == 4] = 3
# In binary-class setting, some masks are encoded as 0, 255
if np.max(input_mask) == 255:
input_mask = np.uint8(input_mask / input_mask.max())
# Chest and GMSC: name inconsistentcy
if 'chest' in dataset:
im_name = im_name.replace('_mask', '')
if 'gmsc' in dataset:
im_name = im_name.replace('mask', 'image').replace(target+'-', '')
try:
input_image = Image.open(os.path.join(input_img_dir, im_name)).convert("RGB")
except:
print('Cannot read image', im_name)
continue
input_array = np.array(input_image)
input_array = np.uint8(input_array / np.max(input_array) * 255)
print('Number of labels', np.max(input_mask))
print('Image maximum', np.max(input_array))
# if we want to do multi-class classification
# else, we combine all the masks as the same class
#if args.class_type == 'm':
if num_class > 1:
#mask_one_hot = (np.arange(1, input_mask.max()+1) == input_mask[...,None]).astype(int)
mask_one_hot = (np.arange(1, num_class+1) == input_mask[...,None]).astype(int)
else:
mask_one_hot = np.array(input_mask > 0,dtype=int)
if len(mask_one_hot.shape) < 3:
mask_one_hot = mask_one_hot[:,:,np.newaxis] # height*depth*1, to consistent with multi-class setting
# Start prediction for each class
if args.model == 'sam':
predictor.set_image(input_array)
elif args.model == 'ritm':
predictor.set_input_image(input_array)
# Mask has to be float
pre_mask = np.zeros_like(mask_one_hot, dtype=float)
dc_class_tmp = []
for cls in range(num_class):
dc_prompt_tmp = []
print('Predicting class %s' % cls)
# segment current class as binary segmentation
try:
mask_cls = np.uint8(mask_one_hot[:,:,cls])
except:
print('Mask do not contain this class, skipped')
if num_class == 1:
dc_class_tmp.append(np.nan)
else:
dc_class_tmp.append([np.nan] * args.num_prompt)
continue
if np.sum(mask_cls) == 0:
print('Empty single cls, skipped')
#dc_class_tmp.append(np.nan)
if num_class == 1:
dc_class_tmp.append(np.nan)
else:
dc_class_tmp.append([np.nan] * args.num_prompt)
continue
# ------ Generate prompt by SAM's eval protocol -------#
preds_mask_full, prompts_full,gt_mask_full,input_full = [], [],[],[]
# Calculates the distance to the closest zero pixel for each pixel of the source image.
# Ref from RITM: https://github.com/SamsungLabs/ritm_interactive_segmentation/blob/aa3bb52a77129e477599b5edfd041535bc67b259/isegm/data/points_sampler.py
padded_mask = np.pad(mask_cls, ((1, 1), (1, 1)), 'constant')
dist_img = cv2.distanceTransform(padded_mask, distanceType=cv2.DIST_L2, maskSize=5).astype(np.float32)[1:-1, 1:-1]
# NOTE: numpy and opencv have inverse definition of row and column
# NOTE: SAM and opencv have the same definition
cY, cX = np.where(dist_img==dist_img.max())
# NOTE: random seems to change DC by +/-1e-4
# Random sample one point with largest distance
random_idx = np.random.randint(0, len(cX))
cX, cY = int(cX[random_idx]), int(cY[random_idx])
# First point: farthest from the object boundary
pc = [(cX,cY)]
pl = [1]
if args.model == 'sam':
preds, _, _ = predictor.predict(point_coords=np.array(pc), point_labels=np.array(pl), return_logits=True)
elif args.model == 'ritm':
# RITM returns mask, mask_prob, iou
click_list = [Click(is_positive=True, coords=(cY, cX), indx = 0)]
_, preds = is_evaluate_sample_onepass(predictor, click_list)
# RITM uses 0.49 as threshold. Substract it to let 0 be the threshold
preds = preds - 0.49
preds = preds[None,:,:].repeat(3,0)
elif args.model == 'sc' or args.model == 'fc':
# SimpleClick
click_list = [Click(is_positive=True, coords=(cY, cX), indx = 0)]
_, preds_prob, _ = is_evaluate_sample_onepass(input_array, mask_cls, predictor, click_list, \
pred_thr=0.49, iterative=False)
preds = preds_prob - 0.49
preds = preds[None,:,:].repeat(3,0)
#elif args.model == 'fc':
# click_list = [Click(is_positive=True, coords=(cY, cX), indx = 0)]
# _, preds_prob, _ = is_evaluate_sample_onepass(input_array, mask_cls, predictor, click_list, \
# pred_thr=0.49, iterative=False)
# preds = preds_prob - 0.49
# if logit < 0, it is more like a background
preds[preds < 0] = 0
preds = preds.transpose((1,2,0))
if args.oracle:
max_slice, max_dc = -1, 0
for mask_slice in range(preds.shape[-1]):
preds_mask_single = np.array(preds[:,:,mask_slice]>0,dtype=int)
dc = IOUMulti(preds_mask_single, mask_cls)
if dc > max_dc:
max_dc = dc
max_slice = mask_slice
print(mask_slice, dc)
preds_mask_single = np.array(preds[:,:,max_slice]>0,dtype=int)
else:
preds_mask_single = np.array(preds[:,:,0]>0,dtype=int)
dc = IOUMulti(preds_mask_single, mask_cls)
dc_prompt_tmp.append(dc)
preds_mask_full.append(np.expand_dims(preds, 0))
gt_mask_full.append(np.expand_dims(mask_cls, 0))
input_full.append(input_array)
prompts_full.append((cX,cY,1))
# Subsequent point: farthest from the boundary of the error region
for idx_p in range(args.num_prompt - 1):
error_mask = np.uint8(np.bitwise_xor(mask_cls, preds_mask_single))
padded_mask = np.pad(error_mask, ((1, 1), (1, 1)), 'constant')
dist_img = cv2.distanceTransform(padded_mask, distanceType=cv2.DIST_L2, maskSize=5).astype(np.float32)[1:-1, 1:-1]
cY, cX = np.where(dist_img==dist_img.max())
random_idx = np.random.randint(0, len(cX))
cX, cY = int(cX[random_idx]), int(cY[random_idx])
pc.append((cX, cY))
if np.sum(input_mask[cY][cX]) == 0:
pl.append(0)
prompts_full.append((cX,cY,0))
else:
pl.append(1)
prompts_full.append((cX,cY,1))
if args.model == 'sam':
preds, _, _ = predictor.predict(point_coords=np.array(pc), point_labels=np.array(pl), return_logits=True)
elif args.model == 'ritm':
curr_click = Click(is_positive=pl[-1], coords=(cY, cX), indx = idx_p+1)
click_list.append(curr_click)
_, preds = is_evaluate_sample_onepass(predictor, click_list)
preds = preds - 0.49
preds = preds[None,:,:].repeat(3,0)
elif args.model == 'sc' or args.model == 'fc':
curr_click = Click(is_positive=pl[-1], coords=(cY, cX), indx = idx_p+1)
click_list.append(curr_click)
# SimpleClick
_, preds_prob, _ = is_evaluate_sample_onepass(input_array, mask_cls, predictor, click_list, \
pred_thr=0.49, iterative=False)
preds = preds_prob - 0.49
preds = preds[None,:,:].repeat(3,0)
# if logit < 0, it is more like a background
preds[preds < 0] = 0
preds = preds.transpose((1,2,0))
if args.oracle:
max_slice, max_dc = -1, 0
for mask_slice in range(preds.shape[-1]):
preds_mask_single = np.array(preds[:,:,mask_slice]>0,dtype=int)
dc = IOUMulti(preds_mask_single, mask_cls)
if dc > max_dc:
max_dc = dc
max_slice = mask_slice
preds_mask_single = np.array(preds[:,:,max_slice]>0,dtype=int)
else:
preds_mask_single = np.array(preds[:,:,0]>0,dtype=int)
dc = IOUMulti(preds_mask_single, mask_cls)
dc_prompt_tmp.append(dc)
preds_mask_full.append(np.expand_dims(preds, 0))
gt_mask_full.append(np.expand_dims(mask_cls, 0))
input_full.append(input_array)
print('Final prompts', pc, pl)
# assgin final mask for this class to it
print('Predicted DC', dc)
dc_class_tmp.append(dc_prompt_tmp)
pre_mask[:,:,cls] = preds[:,:,0]
dc_log.append(dc_class_tmp)
names.append(im_name)
print('****')
# VIS mode only saves mask and prompt information
if vis:
# Final shape: N*H*W*3
# N = number of predictions. 1 if box prompt, otherwise number of prompts
# H,W = size of mask
# 3 = number of outputs per prediction. SAM returns 3 outpus per prompt.
# If no oracle mode, select 0
# If oracle mode, select maximum slice.
# You can do that later, or use variable "max_slice"
preds_mask_full = np.concatenate(preds_mask_full)
gt_mask_full = np.concatenate(gt_mask_full)
input_full = np.concatenate(input_full)
# If box: N*4, N=number of boxes, 4=box coordinate in XYXY format
# If prompts:N*3, N=number of prmts, 3=cX, cY, pos/neg
prompts_full = np.array(prompts_full)
print(preds_mask_full.shape)
# TODO: replace with desired storage place
if not os.path.exists(save_path):
os.mkdir(save_path)
np.save(save_path+'/%s_pred.npy' % im_name[:-4], preds_mask_full)
np.save(save_path+'/%s_prompt.npy' % im_name[:-4], prompts_full)
np.save(save_path+'/%s_gt.npy' % im_name[:-4], gt_mask_full)
np.save(save_path+'/%s_input.npy' % im_name[:-4], input_full)
if not vis:
dc_log = np.array(dc_log)
print(dc_log.shape)
print(np.nanmean(dc_log, axis=0))
print(np.nanmean(dc_log))
version = 'sam_prompt'
#version = 'sam_oracle'
#version = 'sam_box'
if args.model == 'sc':
version = 'simpleclick'
if args.model == 'fc':
version = 'focalclick'
if args.model == 'ritm':
version = 'ritm'
json.dump(names, open('scores/v1_rerun/%s_binary_names_%s.json' % (version, dataset), 'w+'))
np.save('scores/v1_rerun/%s_binary_score_%s.npy' % (version, dataset), dc_log)
================================================
FILE: prompt_gen_and_exec_v2_allmode.py
================================================
from segment_anything import SamPredictor, sam_model_registry
from PIL import Image, ImageDraw, ImageOps
from shapely.geometry import LineString, MultiLineString, Polygon, Point, GeometryCollection
from skimage.morphology import medial_axis
from scipy.optimize import minimize_scalar
from scipy.ndimage import binary_dilation
from skimage.measure import label
from sklearn.cluster import KMeans
import argparse
import os
import cv2
import json
import imutils
import random
import matplotlib.pyplot as plt
import numpy as np
# Fix randomness in prompt selection
np.random.seed(1)
#This is a helper function that should not be called directly
def _find_closest(centroid, pos_points):
dist_squared = np.sum((pos_points - centroid)**2, axis=1)
point_idx = np.argmin(dist_squared)
return pos_points[point_idx]
def IOU(pm, gt):
a = np.sum(np.bitwise_and(pm, gt))
b = np.sum(pm) + np.sum(gt) - a #+ 1e-8
if b == 0:
return -1
else:
return a / b
def IOUMulti(y_pred, y):
score = 0
numLabels = np.max(y)
if np.max(y) == 1:
score = IOU(y_pred, y)
return score
else:
count = 1
for index in range(1,numLabels+1):
curr_score = IOU(y_pred[y==index], y[y==index])
print(index, curr_score)
if curr_score != -1:
score += curr_score
count += 1
return score / (count - 1) # taking average
####################################################
# input: raw_msk
# A mask should containing no 'void' class.
# Binary mask should have value {0,1} but not {0,255}
# output:
# A list of region profiles; Each profile takes the form
# {'loc':[x0,y0,x1,y1], 'cls': cls}
# 'loc' is a list with 4 elements ; 'cls' is object class as integer
####################################################
def MaskToBoxSimple(mask):
mask = mask.squeeze()
#find coordinates of points in the region
row, col = np.argwhere(mask).T
# find the four corner coordinates
y0,x0 = row.min(),col.min()
y1,x1 = row.max(),col.max()
return [x0,y0,x1,y1]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="SAG segmentor for medical images")
parser.add_argument("--num-prompt", default=1, type=int, help="number of prompts to include, negative number means using box as prompts")
parser.add_argument("--class-type", default="b", type=str, help="binary or multi class, choose b or m")
parser.add_argument("--model-path", default="./", type=str, help="the path of the model saved")
parser.add_argument("--init-path", default="./", type=str, help="the path of the dataset")
parser.add_argument("--model", default="sam", type=str, help="the model to use as predictor")
parser.add_argument("--oracle", default=False, type=bool, help="whether eval in the oracle mode, where best prediction is selected based on GT")
parser.add_argument("--result-image",default="./results",type=str, help="the path to save segmented results")
parser.add_argument("--result-score",default="./scores",type=str, help="the path to save result metrics")
args = parser.parse_args()
# Set up model
sam = sam_model_registry["default"](checkpoint=os.path.join(args.model_path, "sam_vit_h_4b8939.pth"))
sam.to('cuda')
predictor = SamPredictor(sam)
# Set up dataset
dataset = input("Type of input: ")
if dataset == 'all':
# all
dataset_list = ['busi', 'breast_b', 'breast_d', 'chest', 'gmsc_sp', 'gmsc_gm', 'heart', 'liver', 'petwhole', 'prostate', 'brats_3m', 'xrayhip', \
'ctliver', 'ctorgan', 'ctcolon', 'cthepaticvessel', 'ctpancreas', 'ctspleen', 'usmuscle', 'usnerve', 'usovariantumor']
else:
dataset_list = [dataset]
for dataset in dataset_list:
num_class = 1
if 'gmsc' in dataset:
input_img_dir = os.path.join(args.init_path, 'sa_gmsc/images')
input_seg_dir = os.path.join(args.init_path, 'sa_gmsc/masks')
elif 'breast' in dataset:
input_img_dir = "../sa_dbc-2D/imgs"
if dataset == 'breast_b':
input_seg_dir = "../sa_dbc-2D/masks_breast"
else:
input_seg_dir = "../sa_dbc-2D/masks_dense-tissue"
else:
input_img_dir = os.path.join(args.init_path, 'sa_%s/images' % dataset)
input_seg_dir = os.path.join(args.init_path, 'sa_%s/masks' % dataset)
# Handle dataset with multi-class
if dataset == 'brats_3m':
num_class = 3
if dataset == 'xrayhip':
num_class = 2
if dataset == 'ctorgan':
num_class = 5
# target is a variable only used by GMSC
if dataset == 'gmsc_sp':
target = 'sp'
if dataset == 'gmsc_gm':
target = 'gm'
print(input_img_dir)
print(input_seg_dir)
# Running
dc_log, names = [], []
mask_list = os.listdir(input_seg_dir)
print('# of dataset', len(mask_list))
# VIS: now VIS function is separted into another file. Only provide mask if neede
vis = False
# Change to [name1, name2, ...] if only need to run on a few samples
im_list = None#['CHNCXR_0061_0_mask.png']
for im_idx, im_name in enumerate(mask_list):
# Skip non-selected images if specified
print(im_name)
if im_list is not None:
if im_name not in im_list:
continue
# GMSC: All masks in the same dir, separated by names
if 'gmsc' in dataset:
if target not in im_name:
continue
if 'DS_Store' in im_name:
continue
# Read image and mask
try:
input_mask = cv2.imread(os.path.join(input_seg_dir, im_name), 0)
except:
print('Cannot read mask', im_name)
continue
if np.max(input_mask) == 0:
print('Empty mask')
print('*****')
continue
# In multi-class setting, we assume classes are labeled 0,1,2,3...
# BraTS has label 1,2,4
if 'brats' in dataset:
input_mask[input_mask == 4] = 3
# In binary-class setting, some masks are encoded as 0, 255
if np.max(input_mask) == 255:
input_mask = np.uint8(input_mask / input_mask.max())
# Chest and GMSC: name inconsistentcy
if 'chest' in dataset:
im_name = im_name.replace('_mask', '')
if 'gmsc' in dataset:
im_name = im_name.replace('mask', 'image').replace(target+'-', '')
try:
input_image = Image.open(os.path.join(input_img_dir, im_name)).convert("RGB")
except:
print('Cannot read image', im_name)
continue
input_array = np.array(input_image)
input_array = np.uint8(input_array / np.max(input_array) * 255)
print('Number of labels', np.max(input_mask))
print('Image maximum', np.max(input_array))
# if we want to do multi-class classification
# else, we combine all the masks as the same class
#if args.class_type == 'm':
if num_class > 1:
#mask_one_hot = (np.arange(1, input_mask.max()+1) == input_mask[...,None]).astype(int)
mask_one_hot = (np.arange(1, num_class+1) == input_mask[...,None]).astype(int)
else:
mask_one_hot = np.array(input_mask > 0,dtype=int)
if len(mask_one_hot.shape) < 3:
mask_one_hot = mask_one_hot[:,:,np.newaxis] # height*depth*1, to consistent with multi-class setting
# Start prediction for each class
predictor.set_image(input_array)
# Mask has to be float
dc_class_tmp = []
for cls in range(num_class):
dc_prompt_tmp = []
# Cls = 2 means to predict mask with label 3
# But BraTS use 1,2,4 to label differet classes
#if cls == 2 and 'brats' in dataset:
# cls += 1
print('Predicting class %s' % cls)
# segment current class as binary segmentation
try:
mask_cls = np.uint8(mask_one_hot[:,:,cls])
except:
print('Mask do not contain this class, skipped')
if num_class == 1:
dc_class_tmp.append(np.nan)
else:
# Fixed with 5 modes for now
dc_class_tmp.append([np.nan] * 5)
continue
if np.sum(mask_cls) == 0:
print('Empty single cls, skipped')
#dc_class_tmp.append(np.nan)
if num_class == 1:
dc_class_tmp.append(np.nan)
else:
dc_class_tmp.append([np.nan] * 5)
continue
# ------ Generate prompt by our definition -------- #
preds_mask_full, prompts_full = [], []
# Find all disconnected regions
label_msk, region_ids = label(mask_cls, connectivity=2, return_num=True)
print('num of regions found', region_ids)
ratio_list, regionid_list = [], []
for region_id in range(1, region_ids+1):
#find coordinates of points in the region
binary_msk = np.where(label_msk==region_id, 1, 0)
# clean some region that is abnormally small
r = np.sum(binary_msk) / np.sum(mask_cls)
print('curr mask over all mask ratio', r)
ratio_list.append(r)
regionid_list.append(region_id)
ratio_list, regionid_list = zip(*sorted(zip(ratio_list, regionid_list)))
regionid_list = regionid_list[::-1]
# 5 modes for now
for mode in range(5):
# Mode 0: middle point of LARGEST mask
if mode == 0:
binary_msk = np.where(label_msk==regionid_list[0], 1, 0)
# Calculates the distance to the closest zero pixel for each pixel of the source image.
# Ref from RITM: https://github.com/SamsungLabs/ritm_interactive_segmentation/blob/aa3bb52a77129e477599b5edfd041535bc67b259/isegm/data/points_sampler.py
# NOTE: numpy and opencv have inverse definition of row and column
# NOTE: SAM and opencv have the same definition
padded_mask = np.uint8(np.pad(binary_msk, ((1, 1), (1, 1)), 'constant'))
dist_img = cv2.distanceTransform(padded_mask, distanceType=cv2.DIST_L2, maskSize=5).astype(np.float32)[1:-1, 1:-1]
cY, cX = np.where(dist_img==dist_img.max())
random_idx = np.random.randint(0, len(cX))
cX, cY = int(cX[random_idx]), int(cY[random_idx])
prompt = [(cX,cY,1)]
# Mode 1: middle point of top-3 LARGEST mask
if mode == 1:
prompt = []
for mask_idx in range(3):
if mask_idx < len(regionid_list):
binary_msk = np.where(label_msk==regionid_list[mask_idx], 1, 0)
padded_mask = np.uint8(np.pad(binary_msk, ((1, 1), (1, 1)), 'constant'))
dist_img = cv2.distanceTransform(padded_mask, distanceType=cv2.DIST_L2, maskSize=5).astype(np.float32)[1:-1, 1:-1]
cY, cX = np.where(dist_img==dist_img.max())
random_idx = np.random.randint(0, len(cX))
cX, cY = int(cX[random_idx]), int(cY[random_idx])
prompt.append((cX,cY,1))
# Mode 2: box of LARGEST mask
if mode == 2:
binary_msk = np.where(label_msk==regionid_list[0], 1, 0)
box = MaskToBoxSimple(binary_msk)
prompt = box
# Mode 3: box of top-3 LARGEST mask
if mode == 3:
prompt = []
for mask_idx in range(3):
if mask_idx < len(regionid_list):
binary_msk = np.where(label_msk==regionid_list[mask_idx], 1, 0)
box = MaskToBoxSimple(binary_msk)
prompt.append(box)
# Mode 4: box of ENTIRE mask
if mode == 4:
box = MaskToBoxSimple(mask_cls)
prompt = box
# Get output based on prompt type
prompt = np.array(prompt)
print('mode %s: prompt: %s' % (mode, prompt))
if prompt.shape[-1] == 3:
pc = prompt[:,:2]
pl = prompt[:, -1]
preds, _, _ = predictor.predict(point_coords=pc, point_labels=pl)
elif prompt.shape[-1] == 4:
if len(prompt.shape) == 1:
preds, _, _ = predictor.predict(box=prompt)
else:
preds = None
for box in prompt:
preds_single, _, _ = predictor.predict(box=box)
if preds is None:
preds = preds_single
else:
preds += preds_single
preds = preds.transpose((1,2,0))
if args.oracle:
max_slice, max_dc = -1, 0
for mask_slice in range(preds.shape[-1]):
preds_mask_single = np.array(preds[:,:,mask_slice]>0,dtype=int)
dc = IOUMulti(preds_mask_single, mask_cls)
if dc > max_dc:
max_dc = dc
max_slice = mask_slice
print(mask_slice, dc)
preds_mask_single = np.array(preds[:,:,max_slice]>0,dtype=int)
else:
preds_mask_single = np.array(preds[:,:,0]>0,dtype=int)
dc = IOUMulti(preds_mask_single, mask_cls)
dc_prompt_tmp.append(dc)
print('IoU:', dc)
# Track prediction, only used when vis
if vis:
preds_mask_full.append(np.expand_dims(preds, 0))
prompts_full.append(prompt)
# assgin final mask for this class to it
dc_class_tmp.append(dc_prompt_tmp)
dc_log.append(dc_class_tmp)
names.append(im_name)
print('****')
# VIS mode only saves mask and prompt information
if vis:
# Final shape: N*H*W*3
# N = number of predictions. 1 if box prompt, otherwise number of prompts
# H,W = size of mask
# 3 = number of outputs per prediction. SAM returns 3 outpus per prompt.
# If no oracle mode, select 0
# If oracle mode, select maximum slice.
# You can do that later, or use variable "max_slice"
preds_mask_full = np.concatenate(preds_mask_full)
# If box: N*4, N=number of boxes, 4=box coordinate in XYXY format
# If prompts:N*3, N=number of prmts, 3=cX, cY, pos/neg
prompts_full = np.array(prompts_full)
print(preds_mask_full.shape)
# TODO: replace with desired storage place
np.save('tmp/%s_pred.npy' % im_name[:-4], preds_mask_full)
np.save('tmp/%s_prompt.npy' % im_name[:-4], prompts_full)
if not vis:
# BRATS labelled class as 1,2,4
dc_log = np.array(dc_log)
print(dc_log.shape)
print(np.nanmean(dc_log, axis=0))
print(np.nanmean(dc_log))
version = 'sam_diffmode'
if args.oracle:
version += '_oracle'
json.dump(names, open('scores/v2/%s_binary_names_%s.json' % (version, dataset), 'w+'))
np.save('scores/v2/%s_binary_score_%s.npy' % (version, dataset), dc_log)
gitextract_s8s34ac6/ ├── .gitignore ├── CITATION.md ├── LICENSE ├── README.md ├── experimental_results_tables/ │ ├── Fig2-Performance of SAM for 5 modes of Use.csv │ ├── fig34-Table_for_focalclick_point_number_changes.csv │ ├── fig34-Table_for_ritm_point_number_changes.csv │ ├── fig34-Table_for_sam_oracle_point_number_changes.csv │ ├── fig34-Table_for_sam_point_number_changes.csv │ ├── fig34-Table_for_simpleclick_point_number_changes.csv │ └── fig4-Table_average_overalldatasets_point_numer_changes.csv ├── prompt_gen_and_exec_v1.py └── prompt_gen_and_exec_v2_allmode.py
SYMBOL INDEX (9 symbols across 2 files) FILE: prompt_gen_and_exec_v1.py function _find_closest (line 31) | def _find_closest(centroid, pos_points): function IOU (line 36) | def IOU(pm, gt): function IOUMulti (line 44) | def IOUMulti(y_pred, y): function MaskToBoxes (line 69) | def MaskToBoxes(mask): function Mask2Points (line 97) | def Mask2Points(raw_msk, N=1): FILE: prompt_gen_and_exec_v2_allmode.py function _find_closest (line 22) | def _find_closest(centroid, pos_points): function IOU (line 27) | def IOU(pm, gt): function IOUMulti (line 35) | def IOUMulti(y_pred, y): function MaskToBoxSimple (line 60) | def MaskToBoxSimple(mask):
Condensed preview — 13 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (83K chars).
[
{
"path": ".gitignore",
"chars": 147,
"preview": "*.pth\n*.py.swp\nrun_record.py\nscores*/*\n.ipynb*/*\nlog.txt\nresults/*\nsegment-anything/*\nsegment_anything/*\nritm_interactiv"
},
{
"path": "CITATION.md",
"chars": 353,
"preview": "```bib\n@article{mazurowski2023segment,\n title={Segment anything model for medical image analysis: an experimental study"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 3472,
"preview": "# Segment Anything Model for Medical Image Analysis: an Experimental Study\n\n[![arXiv Paper](https://img.shields.io/badge"
},
{
"path": "experimental_results_tables/Fig2-Performance of SAM for 5 modes of Use.csv",
"chars": 6226,
"preview": ",Dataset_names,Mode 1: 1 point at largest object region,Mode 1 (oracle): 1 point at largest object region,Mode 2: 1 poin"
},
{
"path": "experimental_results_tables/fig34-Table_for_focalclick_point_number_changes.csv",
"chars": 3426,
"preview": "Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Bre"
},
{
"path": "experimental_results_tables/fig34-Table_for_ritm_point_number_changes.csv",
"chars": 3413,
"preview": "Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Bre"
},
{
"path": "experimental_results_tables/fig34-Table_for_sam_oracle_point_number_changes.csv",
"chars": 3423,
"preview": "Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Bre"
},
{
"path": "experimental_results_tables/fig34-Table_for_sam_point_number_changes.csv",
"chars": 3417,
"preview": "Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Bre"
},
{
"path": "experimental_results_tables/fig34-Table_for_simpleclick_point_number_changes.csv",
"chars": 3412,
"preview": "Num of points,MRI-Spine: GM,MRI-Spine: SC,MRI-Heart,MRI-Prostate,MRI-Brain: Core,MRI-Brain: Edema,MRI-Brain: GD,MRI-Bre"
},
{
"path": "experimental_results_tables/fig4-Table_average_overalldatasets_point_numer_changes.csv",
"chars": 619,
"preview": "Num of points,SAM,SAM (oracle),RITM,SimpleClick,FocalClick\r\n1,0.459519799,0.508014549,0.132232515,0.191030481,0.22402222"
},
{
"path": "prompt_gen_and_exec_v1.py",
"chars": 23652,
"preview": "from segment_anything import SamPredictor, sam_model_registry\nfrom PIL import Image, ImageDraw, ImageOps\nfrom shapely.ge"
},
{
"path": "prompt_gen_and_exec_v2_allmode.py",
"chars": 17205,
"preview": "from segment_anything import SamPredictor, sam_model_registry\nfrom PIL import Image, ImageDraw, ImageOps\nfrom shapely.ge"
}
]
About this extraction
This page contains the full source code of the mazurowski-lab/segment-anything-medical-evaluation GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 13 files (78.2 KB), approximately 24.7k tokens, and a symbol index with 9 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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