https://github.com/DeepMed-Lab-ECNU/FS_MedSAM2

https://github.com/DeepMed-Lab-ECNU/FS_MedSAM2

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/DeepMed-Lab-ECNU/FS_MedSAM2
• Owner: DeepMed-Lab-ECNU
• Created: 2024-09-06T04:45:09.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-11T16:30:23.000Z (4 months ago)
• Last Synced: 2024-09-12T01:51:50.625Z (4 months ago)
• Language: Python
• Size: 6.39 MB
• Stars: 9
• Watchers: 0
• Forks: 1
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# FS_MedSAM2: Exploring the Potential of SAM2 for Few-Shot Medical Image Segmentation without Fine-tuning

[arxiv](https://arxiv.org/abs/2409.04298)

## News

We provide `example_data` folder, which contains some example data for anyone who want to try this code.

## Getting Strated

### Data preparation

You can obtain the data by following steps, or directly obtain the data by [Baidu Cloud](https://pan.baidu.com/s/1DPqK3Qps0h5ZU2EzD1kSwg?pwd=t9vu) (password: t9vu).

1. Follow [SSL_ALPNet](https://github.com/cheng-01037/Self-supervised-Fewshot-Medical-Image-Segmentation/tree/2f2a22b74890cb9ad5e56ac234ea02b9f1c7a535) to create SSL_ALPNet project at  `/path/to/SSL_ALPNet`;

2. Process Synapse-CT and CHAOS-MRI datasets follow SSL_ALPNet;

3. Place `validation_wopred.py` in the `/path/to/SSL_ALPNet` directory, update the `saved_npz_path = '/path/to/saved_npz'`, and run it using either `/path/to/SSL_ALPNet/test_ssl_abdominal_ct.sh` or `/path/to/SSL_ALPNet/test_ssl_abdominal_mri.sh`.

### Experiments Reproduction

1. Based on the official repository of [SAM2](https://github.com/facebookresearch/segment-anything-2), deploy SAM2 locally at `/path/to/SAM2`;

2. Move the files and folders from this repository, `/sam2` and those under `/notebooks`, to the corresponding folder at `/path/to/SAM2/sam2` and `/path/to/SAM2/notebooks`. e.g, move `/FS_MedSAM2/sam2/build_fsmedsam2.py` under `/path/to/SAM2/sam2/build_fsmedsam2.py`.

3. After changing the `saved_npz_path = '/path/to/saved_npz'` and `ckpt_path = '/path/to/ckpt'`, eval FS_MedSAM2:

```

cd /path/to/SAM2/notebooks

python infer_fsmedsam2_by_slice.py # infer 1S1Q

python infer_fsmedsam2_by_volume.py # infer S1SFQ from top

python infer_fsmedsam2_by_volume_from_middle.py # infer S1SFQ from middle

```

## Citation

If you find these projects useful, please consider citing:

```bibtex

@misc{bai2024fsmedsam2exploringpotentialsam2,

      title={FS-MedSAM2: Exploring the Potential of SAM2 for Few-Shot Medical Image Segmentation without Fine-tuning}, 

      author={Yunhao Bai and Qinji Yu and Boxiang Yun and Dakai Jin and Yingda Xia and Yan Wang},

      year={2024},

      eprint={2409.04298},

      archivePrefix={arXiv},

      primaryClass={cs.CV},

      url={https://arxiv.org/abs/2409.04298}, 

}

```

## Questions

If you have any questions, welcome contact me at '[email protected]'