Official implementation of Rectified Contrastive Pseudo Supervision in semi-supervised medical image segmentation

Authors:

Xiangyu Zhao, Zengxin Qi, Sheng Wang, Qian Wang, Xuehai Wu, Ying Mao, Lichi Zhang

manuscript link:

• https://arxiv.org/abs/2301.05500 (preprint on arXiv)

This repo contains the implementation of the proposed Rectified Contrastive Pseudo Supervision semi-supervised segmentation method on two public benchmarks in medical images.
If you use our code, please cite the paper:

@article{zhao2023rcps,
title={RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation},
author={Zhao, Xiangyu Zhao and Qi, Zengxin and Wang, Sheng and Wang, Qian and Wu, Xuehai and Mao, Ying and Zhang, Lichi},
journal={arXiv preprint arXiv:2301.05500},
year={2023}
}

✅ Provide code for data preparation
✅ Publish model checkpoints
✅ Publish full training code
✅ Publish code for inference
🔲 Add support for custom data training

Following previous works, we have validated our method on two benchmark datasets, including 2018 Atrial Segmentation Challenge and NIH Pancreas dataset.
It should be noted that we do not have permissions to redistribute the data. Thus, for those who are interested, please follow the instructions below and process the data, or you will get a mismatching result compared with ours.

Atrial Segmentation: http://atriaseg2018.cardiacatlas.org/
Pancreas dataset: https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT

We split the data following previous works. Detailed split could be found in folder data, which are stored in .csv files.

Download the data from the url above, then run the script prepare_la_dataset.py and prepare_pancreas_dataset.py by passing the argments of data location.

Coming soon.

Link: https://drive.google.com/drive/folders/15-2oBw-11bNMhSCRxzLSHisTbyOcD3gv?usp=sharing

Link：https://pan.baidu.com/s/1wNY06gOmxy8lZzcKiYVR-g?pwd=0512
Extraction Code (提取码)：0512

CUDA_VISIBLE_DEVICES=$CUDA_DEVICE_NUMBER torchrun --nproc_per_node=$NUM_GPU train.py --mixed --benchmark --task $TASK --exp_name $EXP_NAME --wandb --entity $USER_NAME

CUDA_VISIBLE_DEVICES=$CUDA_DEVICE_NUMBER torchrun --nproc_per_node=$NUM_GPU eval.py --mixed --benchmark --task $TASK --exp_name $EXP_NAME -pc $CKPT

Our code is adapted from UAMT, SASSNet, DTC, URPC, MC-Net and SSL4MIS. Thanks these authors for their efforts in building the research community in semi-supervised medical image segmentation.