ProMISe: Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
Recent news
(11/13/23) The pretrained ProMISe models and datasets are uploaded.
(11/12/23) The code is uploaded and updated.
Datasets
Here are the datasets that we used in our experiments, which are modified based on the original datasets from Medical Segmentation Decathlon. We used two public datasets, e.g. task 07 and 10 for pancreas and colon tumor segmentations, respectively.
Installation
conda create -n promise python=3.9
conda activate promise
(Optional): 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
Training
python train.py --data colon --data_dir your_data_directory --save_dir to_save_model_and_log
Test
python test.py --data colon --data_dir your_data_directory --save_dir to_save_model_and_log --split test
use pretrained ProMISe. --use_pretrain --pretrain_path /your_downladed_path/colon_pretrain_promise.pth
Tips
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Set "num_worker" based on your cpu to boost the data loading speed, it matters. From my device, loading data takes 30 seconds if num_workers = 1.
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please specify the save_name.
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don't forget to download the pretrained SAM model from SAM-B, and set the path as "checkpoint_sam".
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set "save_prediction" and "save_base_dir" if you want to save inference predictions.
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more details can be viewed in /config/config_args.py
TODO:
- build this page for better instruction.
- Pytorch DistributedDataParallel. The DDP implementation can be viewed in our latest work
Please shoot an email to hao.li.1@vanderbilt.edu for any questions, and I am always happy to help! :)
@article{li2023promise,
title={Promise: Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models},
author={Li, Hao and Liu, Han and Hu, Dewei and Wang, Jiacheng and Oguz, Ipek},
journal={arXiv preprint arXiv:2310.19721},
year={2023}
}
@article{li2023assessing,
title={Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts},
author={Li, Hao and Liu, Han and Hu, Dewei and Wang, Jiacheng and Oguz, Ipek},
journal={arXiv preprint arXiv:2311.07806},
year={2023}
}