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Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation

Pytorch implementation of our MPSCL for adapting semantic segmentation from the MR/CT modality (source domain) to CT/MR modality (target domain).

Paper

Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation
IEEE Journal of Biomedical and Health Informatics (JBHI) Early Access

Please cite our paper if you find it useful for your research.

@ARTICLE{9672690,  
author={Liu, Zhizhe and Zhu, Zhenfeng and Zheng, Shuai and Liu, Yang and Zhou, Jiayu and Zhao, Yao},  
journal={IEEE Journal of Biomedical and Health Informatics},   title={Margin Preserving Self-Paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation},   
year={2022},  
volume={26},  
number={2},  
pages={638-647},  
doi={10.1109/JBHI.2022.3140853}}

Dependencies

This code requires the following

  • Python 3.6
  • Pytorch 1.3.0

Configure Dataset

  • Thanks to SIFA for sharing the pre-processed data. We have changed the tfrecords data to Numpy. Plz download the data from data and put it in ./data folder
  • Plz run ./dataset/create_datalist.py to create the file containing training data path.
  • Plz run ./dataset/create_test_datalist.py to create the file containing testing data path.

Configure Pretrained Model

  • Plz download the pretrained model from pretrained_model and put it in ./pretrained_model folder The pretrained model file contains two folder:

training contains the initialized models of our MPSCL for generating representative category prototypes and informative pseudo-labels, as described in the implementation details of our paper. testing contains the models corresponding to the results in our paper

Training

To train MPSCL

  • cd <root_dir>/MPSCL/scripts/

For MR2CT

  • CUDA_VISIBLE_DEVICES=#device_id# python train.py --cfg ./configs/MPSCL_MR2CT.yml

For CT2MR

  • CUDA_VISIBLE_DEVICES=#device_id# python train.py --cfg ./configs/MPSCL_CT2MR.yml

Testing

To test MPSCL

If you want to test our released pretrained model

  • cd <root_dir>/MPSCL/scripts

For MR2CT

  • CUDA_VISIBLE_DEVICES=#device_id# python test.py --target_modality 'CT' --pretrained_model_pth '../pretrained_model/testing/MPSCL_MR2CT_best.pth'

For CT2MR

  • CUDA_VISIBLE_DEVICES=#device_id# python test.py --target_modality 'MR' --pretrained_model_pth '../pretrained_model/testing/MPSCL_CT2MR_best.pth'

If you want to test your model

For MR2CT

  • CUDA_VISIBLE_DEVICES=#device_id# python test.py --target_modality 'CT' --pretrained_model_pth 'your model path'

For CT2MR

  • CUDA_VISIBLE_DEVICES=#device_id# python test.py --target_modality 'MR' --pretrained_model_pth 'your model path'

Acknowledgements

This codebase is heavily borrowed from AdvEnt and SupContrast