IPLC

IPLC & IPLC+

This is the official pytorch implementation of our MICCAI 2024 paper "IPLC: Iterative Pseudo Label Correction Guided by SAM for Source-Free Domain Adaptation in Medical Image Segmentation" and extended journal version "IPLC+: SAM-Guided Iterative Pseudo Label Correction for Source-Free Domain Adaptation in Medical Image Segmentation".

To do

• [ ] IPLC+ code
• [x] IPLC code

👉 Requirements

Non-exhaustive list:

• python3.9+
• Pytorch 1.10.1
• nibabel
• Scipy
• NumPy
• Scikit-image
• yaml
• tqdm
• pandas
• scikit-image
• SimpleITK

👉 Usage

1. Download the M&MS Dataset, and organize the dataset directory structure as follows:

   The organized M&MS dataset can be downloaded at Baidu Netdisk.

your/data_root/
       train/
            img/
                A/
                    A0S9V9_0.nii.gz
                    ...
                B/
                C/
                ...
            lab/
                A/
                    A0S9V9_0_gt.nii.gz
                    ...
                B/
                C/
                ...
       valid/
            img/
            lab/
       test/
           img/
           lab/

The network takes nii files as an input. The gt folder contains gray-scale images of the ground-truth, where the gray-scale level is the number of the class (0,1,...K).

2. Download the SAM-Med2D model and move the model to the "your_root/pretrain_model" directory in your project.

3. Train the source model in the source domain, for instance, you can train the source model using domain A on the M&MS dataset:

python train_source.py --config "./config/train2d_source.cfg"

4. Adapt the source model to the target domain, for instance, you can adapt the source model to domain B on the M&MS dataset:

python adapt_main.py --config "./config/adapt.cfg"

Citation

If you find this project useful for your research, please consider citing:

@inproceedings{zhang2024iplc,
  title={IPLC: iterative pseudo label correction guided by SAM for source-free domain adaptation in medical image segmentation},
  author={Zhang, Guoning and Qi, Xiaoran and Yan, Bo and Wang, Guotai},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={351--360},
  year={2024},
  organization={Springer}
}
@article{zhang2025iplc+,
  title={Iplc+: Sam-guided iterative pseudo label correction for source-free domain adaptation in medical image segmentation},
  author={Zhang, Guoning and Qi, Xiaoran and Wu, Jianghao and Yan, Bo and Wang, Guotai},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2025},
  publisher={IEEE}
}

🤝 Acknowledgement

• Thanks to the open-source of the following projects: Segment Anything; SAM-Med2D