Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

by Yicheng Wu*, Zhonghua Wu, Qianyi Wu, Zongyuan Ge, and Jianfei Cai.

News

<12.06.2022> We provided our pre-trained models on the LA and ACDC datasets, see './SS-Net/pretrained_pth';

<09.06.2022> We released the codes;

Introduction

This repository is for our MICCAI 2022 paper: 'Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation'.

Requirements

This repository is based on PyTorch 1.8.0, CUDA 11.1 and Python 3.8.10. All experiments in our paper were conducted on a single NVIDIA Tesla V100 GPU with an identical experimental setting.

Usage

1. Clone the repo.;

git clone https://github.com/ycwu1997/SS-Net.git

2. Put the data in './SS-Net/data';

3. Train the model;

cd SS-Net
# e.g., for 5% labels on LA
python ./code/train_ss_3d.py --labelnum 4 --gpu 0

4. Test the model;

cd SS-Net
# e.g., for 5% labels on LA
python ./code/test_LA.py --labelnum 4

Citation

If our SS-Net model is useful for your research, please consider citing:

  @inproceedings{wu2022exploring,
    title={Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation},
    author={Wu, Yicheng and Wu, Zhonghua and Wu, Qianyi and Ge, Zongyuan and Cai, Jianfei},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    pages={34--43},
    volume={13435},
    year={2022},    
    doi={10.1007/978-3-031-16443-9\_4},
    organization={Springer, Cham}
    }

Acknowledgements:

Our code is adapted from MC-Net, SemiSeg-Contrastive, VAT, and SSL4MIS. Thanks for these authors for their valuable works and hope our model can promote the relevant research as well.

Questions

If any questions, feel free to contact me at '[email protected]'