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https://github.com/MedICL-VU/ProMISe
[ISBI 2024 Oral] ProMISe: Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
https://github.com/MedICL-VU/ProMISe
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[ISBI 2024 Oral] ProMISe: Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
- Host: GitHub
- URL: https://github.com/MedICL-VU/ProMISe
- Owner: MedICL-VU
- Created: 2023-10-28T19:40:31.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-22T15:24:31.000Z (6 months ago)
- Last Synced: 2024-07-22T18:40:53.142Z (6 months ago)
- Language: Python
- Homepage:
- Size: 193 KB
- Stars: 18
- Watchers: 3
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# ProMISe
[](https://arxiv.org/pdf/2310.19721.pdf)
ProMISe: **Pro**mpt-driven 3D **M**edical **I**mage **Se**gmentation Using Pretrained Image Foundation Models
---------------------------------
**Recent news**
(11/13/23) The [pretrained ProMISe](https://drive.google.com/drive/folders/1Yol2tIaNYVve6JQ3osg2pjDRgwVeS-IF?usp=sharing) models and [datasets](https://drive.google.com/drive/folders/13uGNb2WQhSQcBQIUhnvYJere1LBYGDsW?usp=sharing) are uploaded.
(11/12/23) The code is uploaded and updated.
---------------------------------
**Datasets**
Here are the [datasets](https://drive.google.com/drive/folders/13uGNb2WQhSQcBQIUhnvYJere1LBYGDsW?usp=sharing) that we used in our experiments, which are modified based on the original datasets from [Medical Segmentation Decathlon](http://medicaldecathlon.com/). 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](https://drive.google.com/drive/folders/1Yol2tIaNYVve6JQ3osg2pjDRgwVeS-IF?usp=sharing).
--use_pretrain --pretrain_path /your_downladed_path/colon_pretrain_promise.pth
**Tips**
- 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.
- please specify the save_name.
- don't forget to download the pretrained SAM model from [SAM-B](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth), and set the path as "checkpoint_sam".
- set "save_prediction" and "save_base_dir" if you want to save inference predictions.
- more details can be viewed in /config/config_args.py
TODO:
1. build this page for better instruction.
2. Pytorch DistributedDataParallel. The DDP implementation can be viewed in our [latest work](https://github.com/MedICL-VU/PRISM)
---------------------------------
Please shoot an email to [email protected] 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}
}
```