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SemiSAM

Official repository of BIBM'24 paper "SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization".

Introduction

• Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts. Although semi-supervised methods can improve the performance by utilizing unlabeled data, there are still gaps between fully supervised methods under extremely limited annotation scenarios.

• We propose a simple yet efficient strategy to explore the usage of the Segment Anything Model (SAM) for enhancing semi-supervised medical image segmentation. Please refer to the paper for more details.

:computer: Usage

1. Clone the repo

git clone https://github.com/YichiZhang98/SemiSAM
cd SemiSAM

2. Put the data in data/2018LA_Seg_Training Set and SAM checkpoint in ckpt/sam_med3d.pth.

3. Train the model

cd code
python train_LA_semisam_mt.py

4. Test the model

python test_LA.py

:books: Citation

If you find this paper useful, please consider citing:

@inproceedings{SemiSAM,
  title={SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization},
  author={Zhang, Yichi and Yang, Jin and Liu, Yuchen and Cheng, Yuan and Qi, Yuan},
  booktitle={2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  year={2024}
}

• Our code is adapted from UA-MT and SAM-Med3D. We thank all the authors for their contribution.