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README.md

@@ -1,3 +1,147 @@
----
-license: mit
----
+# PRISM
+
+[PRISM](https://arxiv.org/abs/2404.15028): A **P**romptable and **R**obust **I**nteractive **S**egmentation **M**odel with Visual Prompts
+
+Placenta application:
+
+[PRISM Lite](https://arxiv.org/abs/2408.05372): A lightweight model for interactive 3D placenta segmentation in ultrasound
+
+Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images ([arXiv version](https://arxiv.org/abs/2407.08020))
+
+
+## News
+[07/07/24] Check out the decent performance/version of [PRISM on placenta segmentation in ultrasound images](https://github.com/MedICL-VU/PRISM-placenta).
+
+[05/13/24] Our work is early accepted by MICCAI 2024.
+
+[03/07/24] The [pretrained PRISM](https://drive.google.com/drive/u/1/folders/1B6Df44Gd9PEBGPkE1FwC8Ds4jefCekUB) models and [preprocessed datasets](https://drive.google.com/drive/folders/13uGNb2WQhSQcBQIUhnvYJere1LBYGDsW?usp=sharing) are uploaded.
+
+## TODO
+
+
+demo (gradio)
+
+
+
+## Introduction of PRISM
+<img src='figs/framework_v1.png' width='600'>
+
+PRISM is a robust model/method for interactive segmentation in medical imaging. We strive for human-level performance, as a human-in-loop interactive segmentation model with prompts should gradually refine its outcomes until they closely match inter-rater variability. 
+
+
+
+## PRISM tumor segmentation examples
+Briefly, PRISM produces tumor segmentation with mean Dice values of **93.79 (colon), 94.48 (pancreas), 94.18 (liver), and 96.58 (kidney)**.
+
+  |   |   |
+:-------------------------:|:-------------------------:
+Iterative correction for colon tumor | ![iterative_colon](figs/iterative_results.png)
+Iterative correction for multiple tumors | ![iterative_all](figs/iterative_results_supp.png)
+Qualitative results with compared methods | ![qualitative_results](figs/qualitative_results.png)
+
+The quantitative results can be viewed in our [paper](https://arxiv.org/abs/2404.15028).
+
+## Datasets
+The anatomical differences among individuals and ambiguous boundaries are present in the datasets.
+
+- Our preprocessed
+  
+     We used four public [datasets](https://drive.google.com/drive/folders/13uGNb2WQhSQcBQIUhnvYJere1LBYGDsW?usp=sharing) for 3D tumor segmentation in [colon](https://drive.google.com/drive/u/1/folders/1bt17794HCZfmJ2MLh5w0Y_IAJyUj6ti2), [pancreas](https://drive.google.com/drive/u/1/folders/1NncGDG5Cu795WJTmBse-Lm0GrJmtvTdc), [liver](https://drive.google.com/drive/u/1/folders/1vDM2VkNAT5dvFX5XTRhPe6b7zwYWqU_U) and [kidney](https://drive.google.com/drive/u/1/folders/12UDho-JEZHfK1c1laD5dBFNxvJumcoDF). 
+
+- Original
+ 
+     Here are the links for the datasets: [MSD-colon](http://medicaldecathlon.com/), [MSD-pancreas](http://medicaldecathlon.com/), [LiTS2017](https://competitions.codalab.org/competitions/17094) and [KiTS2021](https://kits-challenge.org/kits21/).
+
+
+
+
+
+## Models
+| colon | pancreas | liver | kidney |
+|------------------------------|------------------------------|------------------------------|------------------------------|
+| [Download](https://drive.google.com/drive/u/1/folders/1nPUC0cCsyA_w-tKkhL_Bw7lesBorGzCl) |[Download](https://drive.google.com/drive/u/1/folders/1JPiF7wtSnbFdl0ZLmFQt1b4H-XH4FDrM)| [Download](https://drive.google.com/drive/u/1/folders/1JAFOca1FxWebzZjRa1lKo1OAv0HXqeh6) |[Download](https://drive.google.com/drive/u/1/folders/1sN0HQLM-LfWB5Kp119YwMsZIfv3VJj7S)|
+
+
+## Get Started
+
+**Installation**
+```
+conda create -n prism python=3.9
+conda activate prism
+sudo install git
+pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113 # install pytorch
+pip install git+https://github.com/facebookresearch/segment-anything.git # install segment anything packages
+pip install git+https://github.com/deepmind/surface-distance.git # for normalized surface dice (NSD) evaluation
+pip install -r requirements.txt
+```
+
+
+**Train**
+
+```
+python train.py --data colon --data_dir your_data_directory --save_name your_save_name --multiple_outputs --dynamic --use_box --refine
+```
+
+add "--use_scribble" and "--efficient_scribble" if you want to train with scribbles.
+
+**Train (Distributed Data Parallel)**
+
+the only difference between this and above (train) command is the use of "--ddp".
+```
+python train.py --data colon --data_dir your_data_directory --save_name your_save_name -multiple_outputs --dynamic --use_box --refine --ddp
+```
+
+
+
+**Test**
+
+put downloaded pretrained model under the implementation directory
+```
+python test.py --data colon --data_dir your_data_directory --split test --checkpoint best --save_name prism_pretrain --num_clicks 1 --iter_nums 11 --multiple_outputs --use_box --use_scribble --efficient_scribble --refine --refine_test
+```
+
+
+
+
+**FAQ**
+
+if you got the error as AttributeError: module 'cv2' has no attribute 'ximgproc', please check [this](https://stackoverflow.com/questions/57427233/module-cv2-cv2-has-no-attribute-ximgproc) out
+
+DDP mode has lower Dice and more epoch numbers may solve it
+
+On my end, combining trainer and trainer_basic speeds up
+
+training the model without refine module (as we reported in the paper) has better accuracy than with refine but not using it
+
+
+## License
+
+The model is licensed under the [Apache 2.0 license](LICENSE)
+
+
+## Acknowledgements
+Thanks for the code from: [SAM](https://github.com/facebookresearch/segment-anything), [SAM-Med3D](https://github.com/uni-medical/SAM-Med3D), [ProMISe](https://github.com/MedICL-VU/ProMISe), [ScribblePrompt](https://github.com/halleewong/ScribblePrompt), [nnU-Net](https://github.com/MIC-DKFZ/nnUNet)
+
+If you find this repository useful, please consider citing:
+```
+@inproceedings{li2024prism,
+  title={Prism: A promptable and robust interactive segmentation model with visual prompts},
+  author={Li, Hao and Liu, Han and Hu, Dewei and Wang, Jiacheng and Oguz, Ipek},
+  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
+  pages={389--399},
+  year={2024},
+  organization={Springer}
+}
+```
+```
+@inproceedings{li2024interactive,
+  title={Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images},
+  author={Li, Hao and Oguz, Baris and Arenas, Gabriel and Yao, Xing and Wang, Jiacheng and Pouch, Alison and Byram, Brett and Schwartz, Nadav and Oguz, Ipek},
+  booktitle={International Workshop on Advances in Simplifying Medical Ultrasound},
+  pages={132--142},
+  year={2024},
+  organization={Springer}
+}
+```
+Please send an email to hao.li.1@vanderbilt.edu for any questions and always happy to help! :)
+

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implementation/lits/segmamba_lits/train_segmamba_lits_20251228-205022.log

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+[20:50:22.663] Namespace(data='lits', save_dir='./implementation/lits/segmamba_lits', data_dir='/teamspace/studios/this_studio/lits', num_workers=2, split='train', use_small_dataset=False, model_type='segmamba', lr=4e-05, lr_scheduler='linear', warm_up=False, device='cuda:0', max_epoch=20, image_size=128, batch_size=2, checkpoint='best', checkpoint_sam='./checkpoint_sam/sam_vit_b_01ec64.pth', num_classes=2, tolerance=5, boundary_kernel_size=5, use_pretrain=False, pretrain_path='', resume=False, resume_best=False, ddp=False, gpu_ids=[0, 1], accumulation_steps=20, iter_nums=11, num_clicks=50, num_clicks_validation=10, use_box=True, dynamic_box=False, use_scribble=False, num_multiple_outputs=3, multiple_outputs=True, refine=True, no_detach=False, refine_test=False, dynamic=True, efficient_scribble=False, use_sam3d_turbo=False, save_predictions=False, save_csv=False, save_test_dir='./', save_name='segmamba_lits')
+[20:50:35.673] epoch: 0/20, iter: 0/42: loss:8.5574: rank:-1
+[20:50:38.575] epoch: 0/20, iter: 1/42: loss:8.3818: rank:-1
+[20:50:46.413] epoch: 0/20, iter: 2/42: loss:7.1325: rank:-1
+[20:50:48.770] epoch: 0/20, iter: 3/42: loss:6.2642: rank:-1
+[20:50:57.829] epoch: 0/20, iter: 4/42: loss:6.4799: rank:-1
+[20:51:00.228] epoch: 0/20, iter: 5/42: loss:6.9034: rank:-1
+[20:51:06.002] epoch: 0/20, iter: 6/42: loss:6.7887: rank:-1
+[20:51:08.538] epoch: 0/20, iter: 7/42: loss:6.4513: rank:-1
+[20:51:16.236] epoch: 0/20, iter: 8/42: loss:6.1899: rank:-1
+[20:51:18.960] epoch: 0/20, iter: 9/42: loss:6.0729: rank:-1
+[20:51:27.247] epoch: 0/20, iter: 10/42: loss:5.718: rank:-1
+[20:51:30.102] epoch: 0/20, iter: 11/42: loss:4.9813: rank:-1
+[20:51:39.417] epoch: 0/20, iter: 12/42: loss:5.4939: rank:-1
+[20:51:41.962] epoch: 0/20, iter: 13/42: loss:5.3117: rank:-1
+[20:51:44.396] epoch: 0/20, iter: 14/42: loss:5.449: rank:-1
+[20:51:46.650] epoch: 0/20, iter: 15/42: loss:5.6844: rank:-1
+[20:51:52.956] epoch: 0/20, iter: 16/42: loss:5.3918: rank:-1
+[20:51:55.556] epoch: 0/20, iter: 17/42: loss:4.9375: rank:-1
+[20:52:00.858] epoch: 0/20, iter: 18/42: loss:5.327: rank:-1
+[20:52:03.344] epoch: 0/20, iter: 19/42: loss:4.1737: rank:-1
+[20:52:11.251] epoch: 0/20, iter: 20/42: loss:4.7739: rank:-1
+[20:52:13.459] epoch: 0/20, iter: 21/42: loss:4.5736: rank:-1
+[20:52:22.639] epoch: 0/20, iter: 22/42: loss:4.376: rank:-1
+[20:52:24.957] epoch: 0/20, iter: 23/42: loss:4.7952: rank:-1
+[20:52:32.556] epoch: 0/20, iter: 24/42: loss:4.3449: rank:-1
+[20:52:35.071] epoch: 0/20, iter: 25/42: loss:3.9444: rank:-1
+[20:52:43.794] epoch: 0/20, iter: 26/42: loss:4.4685: rank:-1
+[20:52:46.204] epoch: 0/20, iter: 27/42: loss:4.8515: rank:-1
+[20:52:50.912] epoch: 0/20, iter: 28/42: loss:5.0115: rank:-1
+[20:52:53.284] epoch: 0/20, iter: 29/42: loss:3.5668: rank:-1
+[20:52:57.536] epoch: 0/20, iter: 30/42: loss:4.543: rank:-1
+[20:52:59.859] epoch: 0/20, iter: 31/42: loss:4.5505: rank:-1
+[20:53:06.325] epoch: 0/20, iter: 32/42: loss:4.569: rank:-1
+[20:53:08.813] epoch: 0/20, iter: 33/42: loss:2.9972: rank:-1
+[20:53:16.092] epoch: 0/20, iter: 34/42: loss:4.2197: rank:-1
+[20:53:18.636] epoch: 0/20, iter: 35/42: loss:3.2781: rank:-1
+[20:53:23.086] epoch: 0/20, iter: 36/42: loss:3.7641: rank:-1
+[20:53:25.604] epoch: 0/20, iter: 37/42: loss:3.8031: rank:-1
+[20:53:30.865] epoch: 0/20, iter: 38/42: loss:4.6695: rank:-1
+[20:53:33.241] epoch: 0/20, iter: 39/42: loss:4.6817: rank:-1
+[20:53:36.407] epoch: 0/20, iter: 40/42: loss:2.5221: rank:-1
+[20:53:38.141] epoch: 0/20, iter: 41/42: loss:4.6047: rank:-1
+[20:53:38.141] - Train metrics: 5.1095066

implementation/lits/segmamba_lits/train_segmamba_lits_20251228-205243.log

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+[20:52:43.960] Namespace(data='lits', save_dir='./implementation/lits/segmamba_lits', data_dir='/teamspace/studios/this_studio/lits', num_workers=2, split='train', use_small_dataset=False, model_type='segmamba', lr=4e-05, lr_scheduler='linear', warm_up=False, device='cuda:0', max_epoch=20, image_size=128, batch_size=2, checkpoint='best', checkpoint_sam='./checkpoint_sam/sam_vit_b_01ec64.pth', num_classes=2, tolerance=5, boundary_kernel_size=5, use_pretrain=False, pretrain_path='', resume=False, resume_best=False, ddp=False, gpu_ids=[0, 1], accumulation_steps=20, iter_nums=11, num_clicks=50, num_clicks_validation=10, use_box=True, dynamic_box=False, use_scribble=True, num_multiple_outputs=3, multiple_outputs=True, refine=True, no_detach=False, refine_test=False, dynamic=True, efficient_scribble=True, use_sam3d_turbo=False, save_predictions=False, save_csv=False, save_test_dir='./', save_name='segmamba_lits')

implementation/lits/segmamba_lits/train_segmamba_lits_20251228-205455.log

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+[20:54:55.239] Namespace(data='lits', save_dir='./implementation/lits/segmamba_lits', data_dir='/teamspace/studios/this_studio/lits', num_workers=2, split='train', use_small_dataset=False, model_type='segmamba', lr=4e-05, lr_scheduler='linear', warm_up=False, device='cuda:0', max_epoch=20, image_size=128, batch_size=2, checkpoint='best', checkpoint_sam='./checkpoint_sam/sam_vit_b_01ec64.pth', num_classes=2, tolerance=5, boundary_kernel_size=5, use_pretrain=False, pretrain_path='', resume=False, resume_best=False, ddp=False, gpu_ids=[0, 1], accumulation_steps=20, iter_nums=11, num_clicks=50, num_clicks_validation=10, use_box=True, dynamic_box=False, use_scribble=True, num_multiple_outputs=3, multiple_outputs=True, refine=True, no_detach=False, refine_test=False, dynamic=True, efficient_scribble=True, use_sam3d_turbo=False, save_predictions=False, save_csv=False, save_test_dir='./', save_name='segmamba_lits')

implementation/lits/segmamba_lits/train_segmamba_lits_20251228-205751.log

@@ -0,0 +1,44 @@
+[20:57:51.028] Namespace(data='lits', save_dir='./implementation/lits/segmamba_lits', data_dir='/teamspace/studios/this_studio/lits', num_workers=2, split='train', use_small_dataset=False, model_type='segmamba', lr=4e-05, lr_scheduler='linear', warm_up=False, device='cuda:0', max_epoch=20, image_size=128, batch_size=2, checkpoint='best', checkpoint_sam='./checkpoint_sam/sam_vit_b_01ec64.pth', num_classes=2, tolerance=5, boundary_kernel_size=5, use_pretrain=False, pretrain_path='', resume=False, resume_best=False, ddp=False, gpu_ids=[0, 1], accumulation_steps=20, iter_nums=11, num_clicks=50, num_clicks_validation=10, use_box=True, dynamic_box=False, use_scribble=True, num_multiple_outputs=3, multiple_outputs=True, refine=True, no_detach=False, refine_test=False, dynamic=True, efficient_scribble=True, use_sam3d_turbo=False, save_predictions=False, save_csv=False, save_test_dir='./', save_name='segmamba_lits')
+[20:58:34.483] epoch: 0/20, iter: 0/42: loss:6.7362: rank:-1
+[20:59:03.016] epoch: 0/20, iter: 1/42: loss:6.2911: rank:-1
+[20:59:20.025] epoch: 0/20, iter: 2/42: loss:6.2703: rank:-1
+[20:59:31.861] epoch: 0/20, iter: 3/42: loss:6.9132: rank:-1
+[20:59:43.729] epoch: 0/20, iter: 4/42: loss:6.2347: rank:-1
+[20:59:54.965] epoch: 0/20, iter: 5/42: loss:6.3368: rank:-1
+[21:00:16.486] epoch: 0/20, iter: 6/42: loss:5.1229: rank:-1
+[21:00:36.620] epoch: 0/20, iter: 7/42: loss:4.5165: rank:-1
+[21:00:47.361] epoch: 0/20, iter: 8/42: loss:5.8889: rank:-1
+[21:00:56.361] epoch: 0/20, iter: 9/42: loss:6.1573: rank:-1
+[21:01:19.516] epoch: 0/20, iter: 10/42: loss:4.5671: rank:-1
+[21:01:32.206] epoch: 0/20, iter: 11/42: loss:5.5973: rank:-1
+[21:01:46.110] epoch: 0/20, iter: 12/42: loss:4.5529: rank:-1
+[21:01:57.634] epoch: 0/20, iter: 13/42: loss:4.7768: rank:-1
+[21:02:09.999] epoch: 0/20, iter: 14/42: loss:5.3851: rank:-1
+[21:02:24.057] epoch: 0/20, iter: 15/42: loss:4.5796: rank:-1
+[21:02:35.541] epoch: 0/20, iter: 16/42: loss:4.9914: rank:-1
+[21:02:44.974] epoch: 0/20, iter: 17/42: loss:5.3536: rank:-1
+[21:02:54.089] epoch: 0/20, iter: 18/42: loss:5.2157: rank:-1
+[21:03:11.600] epoch: 0/20, iter: 19/42: loss:4.4662: rank:-1
+[21:03:22.128] epoch: 0/20, iter: 20/42: loss:4.7908: rank:-1
+[21:03:33.573] epoch: 0/20, iter: 21/42: loss:4.1763: rank:-1
+[21:03:52.205] epoch: 0/20, iter: 22/42: loss:4.4056: rank:-1
+[21:04:10.739] epoch: 0/20, iter: 23/42: loss:3.8492: rank:-1
+[21:04:27.835] epoch: 0/20, iter: 24/42: loss:4.091: rank:-1
+[21:04:39.345] epoch: 0/20, iter: 25/42: loss:3.913: rank:-1
+[21:05:02.657] epoch: 0/20, iter: 26/42: loss:4.5026: rank:-1
+[21:05:12.816] epoch: 0/20, iter: 27/42: loss:4.1648: rank:-1
+[21:05:24.934] epoch: 0/20, iter: 28/42: loss:4.3919: rank:-1
+[21:05:33.928] epoch: 0/20, iter: 29/42: loss:4.4407: rank:-1
+[21:05:45.244] epoch: 0/20, iter: 30/42: loss:4.4362: rank:-1
+[21:05:58.859] epoch: 0/20, iter: 31/42: loss:3.9054: rank:-1
+[21:06:08.443] epoch: 0/20, iter: 32/42: loss:4.394: rank:-1
+[21:06:20.210] epoch: 0/20, iter: 33/42: loss:3.8882: rank:-1
+[21:06:30.116] epoch: 0/20, iter: 34/42: loss:4.3971: rank:-1
+[21:06:48.494] epoch: 0/20, iter: 35/42: loss:3.8213: rank:-1
+[21:07:06.562] epoch: 0/20, iter: 36/42: loss:3.538: rank:-1
+[21:07:27.438] epoch: 0/20, iter: 37/42: loss:3.673: rank:-1
+[21:07:42.053] epoch: 0/20, iter: 38/42: loss:3.4293: rank:-1
+[21:08:00.813] epoch: 0/20, iter: 39/42: loss:4.1726: rank:-1
+[21:08:11.390] epoch: 0/20, iter: 40/42: loss:4.2546: rank:-1
+[21:08:20.427] epoch: 0/20, iter: 41/42: loss:3.4214: rank:-1
+[21:08:20.428] - Train metrics: 4.7621613

implementation/lits/segmamba_lits/train_segmamba_lits_20251228-210909.log

@@ -0,0 +1,44 @@
+[21:09:09.858] Namespace(data='lits', save_dir='./implementation/lits/segmamba_lits', data_dir='/teamspace/studios/this_studio/lits', num_workers=2, split='train', use_small_dataset=False, model_type='segmamba', lr=4e-05, lr_scheduler='linear', warm_up=False, device='cuda:0', max_epoch=20, image_size=128, batch_size=2, checkpoint='best', checkpoint_sam='./checkpoint_sam/sam_vit_b_01ec64.pth', num_classes=2, tolerance=5, boundary_kernel_size=5, use_pretrain=False, pretrain_path='', resume=False, resume_best=False, ddp=False, gpu_ids=[0, 1], accumulation_steps=20, iter_nums=11, num_clicks=50, num_clicks_validation=10, use_box=True, dynamic_box=False, use_scribble=True, num_multiple_outputs=3, multiple_outputs=True, refine=True, no_detach=False, refine_test=False, dynamic=True, efficient_scribble=True, use_sam3d_turbo=False, save_predictions=False, save_csv=False, save_test_dir='./', save_name='segmamba_lits')
+[21:09:50.645] epoch: 0/20, iter: 0/42: loss:5.8351: rank:-1
+[21:10:10.653] epoch: 0/20, iter: 1/42: loss:6.1341: rank:-1
+[21:10:33.245] epoch: 0/20, iter: 2/42: loss:6.8898: rank:-1
+[21:10:57.577] epoch: 0/20, iter: 3/42: loss:6.1827: rank:-1
+[21:11:18.892] epoch: 0/20, iter: 4/42: loss:6.1998: rank:-1
+[21:11:44.095] epoch: 0/20, iter: 5/42: loss:4.9246: rank:-1
+[21:11:55.705] epoch: 0/20, iter: 6/42: loss:6.3802: rank:-1
+[21:12:07.959] epoch: 0/20, iter: 7/42: loss:6.0113: rank:-1
+[21:12:26.902] epoch: 0/20, iter: 8/42: loss:4.9691: rank:-1
+[21:12:44.210] epoch: 0/20, iter: 9/42: loss:5.3392: rank:-1
+[21:12:57.610] epoch: 0/20, iter: 10/42: loss:4.8586: rank:-1
+[21:13:09.536] epoch: 0/20, iter: 11/42: loss:5.3768: rank:-1
+[21:13:18.967] epoch: 0/20, iter: 12/42: loss:5.1704: rank:-1
+[21:13:29.575] epoch: 0/20, iter: 13/42: loss:4.6515: rank:-1
+[21:13:44.878] epoch: 0/20, iter: 14/42: loss:5.1376: rank:-1
+[21:13:55.030] epoch: 0/20, iter: 15/42: loss:4.928: rank:-1
+[21:14:04.131] epoch: 0/20, iter: 16/42: loss:4.966: rank:-1
+[21:14:21.901] epoch: 0/20, iter: 17/42: loss:4.8536: rank:-1
+[21:14:39.631] epoch: 0/20, iter: 18/42: loss:3.9355: rank:-1
+[21:14:52.881] epoch: 0/20, iter: 19/42: loss:4.1299: rank:-1
+[21:15:02.687] epoch: 0/20, iter: 20/42: loss:4.8235: rank:-1
+[21:15:15.051] epoch: 0/20, iter: 21/42: loss:4.5646: rank:-1
+[21:15:25.591] epoch: 0/20, iter: 22/42: loss:4.5925: rank:-1
+[21:15:44.577] epoch: 0/20, iter: 23/42: loss:3.6751: rank:-1
+[21:15:57.779] epoch: 0/20, iter: 24/42: loss:3.8956: rank:-1
+[21:16:19.007] epoch: 0/20, iter: 25/42: loss:3.6836: rank:-1
+[21:16:35.698] epoch: 0/20, iter: 26/42: loss:4.0975: rank:-1
+[21:16:50.358] epoch: 0/20, iter: 27/42: loss:4.3446: rank:-1
+[21:17:07.844] epoch: 0/20, iter: 28/42: loss:4.1784: rank:-1
+[21:17:18.526] epoch: 0/20, iter: 29/42: loss:4.3717: rank:-1
+[21:17:33.551] epoch: 0/20, iter: 30/42: loss:4.1323: rank:-1
+[21:17:50.768] epoch: 0/20, iter: 31/42: loss:3.241: rank:-1
+[21:18:03.168] epoch: 0/20, iter: 32/42: loss:4.0124: rank:-1
+[21:18:18.346] epoch: 0/20, iter: 33/42: loss:4.3774: rank:-1
+[21:18:36.772] epoch: 0/20, iter: 34/42: loss:4.8205: rank:-1
+[21:18:47.896] epoch: 0/20, iter: 35/42: loss:4.588: rank:-1
+[21:18:57.899] epoch: 0/20, iter: 36/42: loss:4.0828: rank:-1
+[21:19:17.407] epoch: 0/20, iter: 37/42: loss:2.9969: rank:-1
+[21:19:28.116] epoch: 0/20, iter: 38/42: loss:4.085: rank:-1
+[21:19:49.217] epoch: 0/20, iter: 39/42: loss:4.404: rank:-1
+[21:20:03.205] epoch: 0/20, iter: 40/42: loss:5.6263: rank:-1
+[21:20:12.085] epoch: 0/20, iter: 41/42: loss:3.4617: rank:-1
+[21:20:12.088] - Train metrics: 4.736407

requirements.txt

@@ -0,0 +1,16 @@
+batchgenerators==0.25
+matplotlib==3.7.1
+MedPy==0.4.0
+monai==1.1.0
+nibabel==5.1.0
+numpy==1.24.3
+scipy==1.9.1
+scikit-image
+nibabel
+einops
+pandas
+torchio
+prefetch-generator
+connected-components-3d
+kornia
+opencv-python

splits/README.md

@@ -0,0 +1 @@
+this directory contains the data splits in our experiments

splits/colon/split.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a740fec88f7ebeb88b4b416364adf6fbd3f91e6e7494bc8567ae08a0817950b
+size 20829

splits/kits/split.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:16366dcecac5e87349f320017520416738272848bdfec0807b95dad36b71382e
+size 115818

splits/lits/split.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67bb0a58bb8a01029dd4aadeebf6a804cd13325472ae4e4a7d1ccddd7f5179c6
+size 49971

splits/pancreas/split.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3dc340acaf7ee0218d11864c84ccb5d90f48f786581e548c8b5e1b3a42dd4a7
+size 128678

src/config/config_args.py

@@ -0,0 +1,78 @@
+import argparse
+import os
+import warnings
+parser = argparse.ArgumentParser()
+
+
+# data
+parser.add_argument("--data", default=None, type=str, choices=["kits", "pancreas", "lits", "colon"])
+parser.add_argument("--save_dir", default="./implementation/", type=str)
+parser.add_argument("--data_dir", default="", type=str)
+parser.add_argument("--num_workers", default=2, type=int)
+parser.add_argument("--split", default="train", type=str)
+parser.add_argument('--use_small_dataset', action="store_true")
+
+
+# network
+parser.add_argument('--model_type', type=str, default='vit_b_ori')
+parser.add_argument("--lr", default=4e-5, type=float)
+parser.add_argument("--lr_scheduler", default='linear', type=str, choices=["linear", "exp"])
+parser.add_argument('--warm_up', action="store_true")
+parser.add_argument("--device", default="cuda:0", type=str)
+parser.add_argument("--max_epoch", default=200, type=int)
+parser.add_argument("--image_size", default=128, type=int)
+parser.add_argument("--batch_size", default=1, type=int)
+parser.add_argument("--checkpoint", default="best", type=str)
+parser.add_argument("--checkpoint_sam", default="./checkpoint_sam/sam_vit_b_01ec64.pth", type=str,
+                    help='path of pretrained SAM')
+parser.add_argument("--num_classes", default=2, type=int)
+parser.add_argument("--tolerance", default=5, type=int)
+parser.add_argument("--boundary_kernel_size", default=5, type=int,
+                    help='an integer for kernel size of avepooling layer for boundary generation')
+parser.add_argument("--use_pretrain", action="store_true")
+parser.add_argument("--pretrain_path", default="", type=str)
+parser.add_argument("--resume", action="store_true")
+parser.add_argument("--resume_best", action="store_true")
+parser.add_argument("--ddp", action="store_true")
+parser.add_argument('--gpu_ids', type=int, nargs='+', default=[0, 1])
+parser.add_argument('--accumulation_steps', type=int, default=20)
+
+parser.add_argument('--iter_nums', type=int, default=11)
+parser.add_argument('--num_clicks', type=int, default=50)
+parser.add_argument('--num_clicks_validation', type=int, default=10)
+parser.add_argument('--use_box', action="store_true")
+parser.add_argument('--dynamic_box', action="store_true")
+parser.add_argument('--use_scribble', action="store_true")
+
+
+parser.add_argument('--num_multiple_outputs', type=int, default=3)
+parser.add_argument('--multiple_outputs', action="store_true")
+parser.add_argument('--refine', action="store_true")
+parser.add_argument('--no_detach', action="store_true")
+parser.add_argument('--refine_test', action="store_true")
+
+parser.add_argument('--dynamic', action="store_true")
+parser.add_argument('--efficient_scribble', action="store_true")
+parser.add_argument("--use_sam3d_turbo", action="store_true")
+
+
+
+# saving
+parser.add_argument("--save_predictions", action="store_true")
+parser.add_argument("--save_csv", action="store_true")
+parser.add_argument("--save_test_dir", default='./', type=str)
+parser.add_argument("--save_name", default='testing_only', type=str)
+
+
+
+
+
+
+def check_and_setup_parser(args):
+    if args.save_name == 'testing_only':
+        warnings.warn("[save_name] (--save_name) should be a real name, currently is for testing purpose (--save_name=testing_only)")
+
+
+    args.save_dir = os.path.join(args.save_dir, args.data, args.save_name)
+    if not os.path.exists(args.save_dir):
+        os.makedirs(args.save_dir)

src/config/config_setup.py

@@ -0,0 +1,56 @@
+from src.models.build_sam3D import sam_model_registry3D
+from src.dataset.dataloader import Dataset_promise, Dataloader_promise
+import torchio as tio
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data.distributed import DistributedSampler
+import torch
+def get_dataloader(args, split='', use_small=False):
+    transforms_list = [tio.ToCanonical(), tio.Resample(1), ]
+    if split == 'train':
+        transforms_list.append(tio.RandomFlip(axes=(0, 1, 2)))
+
+    transforms = tio.Compose(transforms_list)
+
+    dataset = Dataset_promise(
+        data=args.data,
+        data_dir=args.data_dir,
+        split=split,
+        transform=transforms,
+        image_size=args.image_size,
+        args=args,
+    )
+
+    batch_size = args.batch_size if split == 'train' else 1
+
+    if split == 'train':
+        train_sampler = None
+        shuffle = True
+        if args.ddp:
+            train_sampler = DistributedSampler(dataset)
+            shuffle = False
+    else:
+        train_sampler = None
+        shuffle = False
+
+    pin_memory = True
+    if split != 'train' and args.data == 'lits':
+        pin_memory = False
+
+    dataloader = Dataloader_promise(
+        dataset=dataset,
+        sampler=train_sampler,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        num_workers=args.num_workers,
+        pin_memory=pin_memory,
+    )
+    return dataloader
+
+
+
+def build_model(args, checkpoint=None):
+    sam_model = sam_model_registry3D[args.model_type](checkpoint=checkpoint, args=args).to(args.device)
+    if args.ddp:
+        sam_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(sam_model)
+        sam_model = DDP(sam_model, device_ids=[args.rank], output_device=args.rank)
+    return sam_model

src/dataset/dataloader.py

@@ -0,0 +1,265 @@
+from torch.utils.data import Dataset
+from torch.utils.data import DataLoader
+import torchio as tio
+import pickle
+import numpy as np
+import os
+import torch
+import SimpleITK as sitk
+from prefetch_generator import BackgroundGenerator
+from monai.transforms import (
+    Compose,
+    RandCropByPosNegLabeld,
+    ScaleIntensityRanged,
+    NormalizeIntensityd,
+    RandShiftIntensityd,
+    RandZoomd,
+)
+import cc3d, math
+
+class Dataset_promise(Dataset):
+    def __init__(self, data, data_dir, split='train', image_size=128, transform=None, pcc=False, args=None):
+        self.args = args
+        self.data = data
+        self.paths = data_dir
+
+        self._set_file_paths(self.paths, split)
+        self._set_dataset_stat()
+
+        self.image_size = (image_size, image_size, image_size)
+        self.transform = transform
+        self.threshold = 0
+        self.split = split
+        self.pcc = pcc
+        self.monai_transforms = self._get_transforms(split=split)
+
+        self.cc = 1
+
+    def __len__(self):
+        return len(self.label_paths)
+
+    def __getitem__(self, index):
+        sitk_image = sitk.ReadImage(self.image_paths[index])
+        sitk_label = sitk.ReadImage(self.label_paths[index])
+
+        if sitk_image.GetOrigin() != sitk_label.GetOrigin():
+            sitk_image.SetOrigin(sitk_label.GetOrigin())
+        if sitk_image.GetDirection() != sitk_label.GetDirection():
+            sitk_image.SetDirection(sitk_label.GetDirection())
+
+        if sitk_image.GetSpacing() != sitk_label.GetSpacing():
+            sitk_label.SetSpacing(sitk_image.GetSpacing())
+
+        subject = tio.Subject(
+            image=tio.ScalarImage.from_sitk(sitk_image),
+            label=tio.LabelMap.from_sitk(sitk_label),
+        )
+
+        subject_save = tio.Subject(
+            image=tio.ScalarImage.from_sitk(sitk_image),
+            label=tio.LabelMap.from_sitk(sitk_label),
+        )
+
+
+        if self.data == 'lits':
+            b = subject.label.data
+            a = tio.CropOrPad._bbox_mask(b[0].cpu().numpy())
+            w, h, d = a[1][0] - a[0][0], a[1][1] - a[0][1], a[1][2] - a[0][2]
+            w, h, d = max(w + 20, 128), max(h + 20, 128), max(d + 20, 128)
+            crop_transform = tio.CropOrPad(mask_name='label', target_shape=(w, h, d))
+            subject = crop_transform(subject)
+            subject_save = crop_transform(subject_save)
+
+
+
+        if self.target_label != 0:
+            subject = self._binary_label(subject)
+            subject_save = self._binary_label(subject_save)
+
+        if self.transform:
+            try:
+                subject = self.transform(subject)
+                subject_save = self.transform(subject_save)
+            except:
+                print(self.image_paths[index])
+
+        if (self.pcc):
+            subject = self._pcc(subject)
+
+
+        if subject.label.data.sum() <= self.threshold:
+            print(self.image_paths[index], 'label volume too small')
+            if self.split == 'train':
+                return self.__getitem__(np.random.randint(self.__len__()))
+                #return self.__getitem__(0)
+            else:
+                if self.data == 'lits':
+                    return subject, self.image_paths[index]
+                else:
+                    return subject.image.data.clone().detach(), subject.label.data.clone().detach(), self.image_paths[index]
+
+
+        if self.split == "train":
+            trans_dict = self.monai_transforms({"image": subject.image.data.clone().detach(),
+                                                "label": subject.label.data.clone().detach()})[0]
+            img_aug, seg_aug = trans_dict["image"], trans_dict["label"]
+            return img_aug.float(), seg_aug.float(), self.image_paths[index]
+        else:
+            if self.data == 'lits':
+                trans_dict = self.monai_transforms({"image": subject.image.data.clone().detach()})
+                subject.image.data = trans_dict["image"]
+                return subject, self.image_paths[index], subject_save
+
+            if self.data == 'kits':
+                subject = self._separate_crop(subject)
+
+            crop_transform = tio.CropOrPad(mask_name='label', target_shape=self.image_size)
+            subject = crop_transform(subject)
+            subject_save = crop_transform(subject_save)
+
+            trans_dict = self.monai_transforms({"image": subject.image.data.clone().detach()})
+            img_aug = trans_dict["image"]
+            return img_aug, subject.label.data.clone().detach(), self.image_paths[index], subject_save
+
+
+    def _separate_crop(self, subject):
+        label = subject.label.data
+        labels_out, N = cc3d.connected_components(label[0].cpu().numpy(), return_N=True)
+        crop_transform = tio.CropOrPad(mask_name='label', target_shape=self.image_size)
+        mid_cut = 0
+
+        if N > 1:
+            label_1, label_2 = torch.zeros_like(label), torch.zeros_like(label)
+
+            # left, right
+            mid_cut = math.ceil(label.size(1) / 2)
+            label_1[0, 0: mid_cut, :], label_2[0, mid_cut: -1, :] = label[0, 0: mid_cut, :], label[0, mid_cut: -1, :] # left, right
+
+
+            image_1, image_2 = subject.image.data, subject.image.data
+
+            subject_1 = tio.Subject(image=tio.ScalarImage(tensor=image_1), label=tio.LabelMap(tensor=label_1))
+            subject_2 = tio.Subject(image=tio.ScalarImage(tensor=image_2), label=tio.LabelMap(tensor=label_2))
+
+            subject_1, subject_2 = crop_transform(subject_1), crop_transform(subject_2)
+
+            # found 2 connected components for some cases (e.g. case 289), use below to eliminate
+            # however, this will bring warnings, but it's okay
+            if torch.unique(subject_2.label.data).size(0) == 1:
+                subject.image.data, subject.label.data = subject_1.image.data, subject_1.label.data
+            elif torch.unique(subject_1.label.data).size(0) == 1:
+                subject.image.data, subject.label.data = subject_2.image.data, subject_2.label.data
+            else:
+                subject.image.data = torch.cat([subject_1.image.data, subject_2.image.data], dim=0)
+                subject.label.data = torch.cat([subject_1.label.data, subject_2.label.data], dim=0)
+        else:
+            subject = crop_transform(subject)
+
+        return subject
+
+    def _set_file_paths(self, data_dir, split):
+        self.image_paths = []
+        self.label_paths = []
+        split_file = "split.pkl"
+        dataset_split = os.path.join(data_dir, split_file)
+        if not os.path.exists(dataset_split):
+            alt_dir = os.path.join(data_dir, "Task01_LITS17")
+            alt_split = os.path.join(alt_dir, split_file)
+            if os.path.exists(alt_split):
+                data_dir = alt_dir
+                dataset_split = alt_split
+        if not os.path.exists(dataset_split):
+            raise FileNotFoundError(f"split.pkl not found under {data_dir}")
+        with open(dataset_split, "rb") as f:
+            d = pickle.load(f)[0][split]
+        self.image_paths = [os.path.join(data_dir, d[i][0].strip("/")) for i in list(d.keys())]
+        self.label_paths = [os.path.join(data_dir, d[i][1].strip("/")) for i in list(d.keys())]
+
+    def _set_dataset_stat(self):
+        self.target_label = 0
+        if self.data == 'colon':
+            self.intensity_range, self.global_mean, self.global_std = (-57, 175), 65.175035, 32.651197
+
+        elif self.data == 'pancreas':
+            self.intensity_range, self.global_mean, self.global_std = (-39, 204), 68.45214, 63.422806
+            self.target_label = 2
+
+        elif self.data == 'lits':
+            self.intensity_range, self.global_mean, self.global_std = (-48, 163), 60.057533, 40.198017
+            self.target_label = 2
+
+        elif self.data == 'kits':
+            self.intensity_range, self.global_mean, self.global_std = (-54, 247), 59.53867, 55.457336
+            self.target_label = 2
+
+
+    def _get_transforms(self, split):
+        if split == "train":
+            transforms = Compose(
+                [
+                    ScaleIntensityRanged(
+                        keys=["image"],
+                        a_min=self.intensity_range[0],
+                        a_max=self.intensity_range[1],
+                        b_min=self.intensity_range[0],
+                        b_max=self.intensity_range[1],
+                        clip=True,
+                    ),
+                    RandCropByPosNegLabeld(
+                        keys=["image", "label"],
+                        spatial_size=(128, 128, 128),
+                        label_key="label",
+                        pos=2,
+                        neg=0,
+                        num_samples=1,
+                    ),
+                    RandShiftIntensityd(keys=["image"], offsets=20, prob=0.5),
+                    NormalizeIntensityd(keys=["image"], subtrahend=self.global_mean, divisor=self.global_std),
+
+                    RandZoomd(keys=["image", "label"], prob=0.8, min_zoom=0.85, max_zoom=1.25,
+                              mode=["trilinear", "nearest"]),
+                    ])
+        else:
+            transforms = Compose(
+                [
+                    ScaleIntensityRanged(
+                        keys=["image"],
+                        a_min=self.intensity_range[0],
+                        a_max=self.intensity_range[1],
+                        b_min=self.intensity_range[0],
+                        b_max=self.intensity_range[1],
+                        clip=True,
+                    ),
+                    NormalizeIntensityd(keys=["image"], subtrahend=self.global_mean, divisor=self.global_std),
+                ]
+            )
+        return transforms
+
+    def _binary_label(self, subject):
+        label = subject.label.data
+        label = (label == self.target_label)
+        subject.label.data = label.float()
+        return subject
+
+    def _pcc(self, subject):
+        print("using pcc setting")
+        # crop from random click point
+        random_index = torch.argwhere(subject.label.data == 1)
+        if (len(random_index) >= 1):
+            random_index = random_index[np.random.randint(0, len(random_index))]
+            # print(random_index)
+            crop_mask = torch.zeros_like(subject.label.data)
+            # print(crop_mask.shape)
+            crop_mask[random_index[0]][random_index[1]][random_index[2]][random_index[3]] = 1
+            subject.add_image(tio.LabelMap(tensor=crop_mask, affine=subject.label.affine), image_name="crop_mask")
+            subject = tio.CropOrPad(mask_name='crop_mask', target_shape=self.image_size)(subject)
+
+        return subject
+
+
+class Dataloader_promise(DataLoader):
+    def __iter__(self):
+        return BackgroundGenerator(super().__iter__())
+
+
+

src/implementation/colon/readme.log

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+put pretrained prism in this directory

src/implementation/kits/readme.log

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+put pretrained prism in this directory

src/implementation/lits/readme.log

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+put pretrained prism in this directory

src/implementation/pancreas/readme.log

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+put pretrained prism in this directory