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CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision

This project is developed for our CVPR 2022 paper: CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision. Our code is implemented based on the PuzzleMix, but is also applicable to other mixup-based augmentation methods. For more information about CycleMix, please read the following paper:

<div align=center><img src="CycleMix.png" width="70%"></div> 
@article{zhang2022cyclemix,
  title={CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision},
  author={Zhang, Ke and Zhuang, Xiahai},
  journal={arXiv preprint arXiv:2203.01475},
  year={2022}
}

Please also cite this paper if you are using CycleMix for your research.

Datasets

  1. The MSCMR dataset with mask annotations can be downloaded from MSCMRseg.
  2. Our scribble annotations of MSCMRseg have been released in MSCMR_scribbles. Please cite this paper if you use the scribbles for your research.
  3. The scribble-annotated MSCMR dataset used for training could be directly downloaded from MSCMR_dataset.
  4. The ACDC dataset with mask annotations can be downloaded from ACDC and the scribble annotations could be downloaded from ACDC scribbles. Please organize the dataset as the following structure:
XXX_dataset/
  -- TestSet/
      --images/
      --labels/
  -- train/
      --images/
      --labels/
  -- val/
      --images/
      --labels/

Usage

  1. Set the "dataset" parameter in main.py, line 76, to the name of dataset, i.e., "MSCMR_dataset".
  2. Set the "output_dir" in main.py, line 79, as the path to save the checkpoints.
  3. Download the dataset, for example, the MSCMR_dataset. Then, Set the dataset path in /data/mscmr.py, line 110, to your data path where the dataset is located in.
  4. Check your GPU devices and modify the "GPU_ids" parameter in main.py, line 83 and "CUDA_VISIBLE_DEVICES" in run.sh.
  5. Start to train by sh run.sh.
CUDA_LAUNCH_BLOCKING=1 CUDA_VISIBLE_DEVICES=5 nohup python main.py --mixup_alpha 0.5 --graph True --n_labels 3 --eta 0.2 --beta 1.2 --gamma 0.5 --neigh_size 4 --transport True --t_size 4 --t_eps 0.8 &

Requirements

This code has been tested with
Python 3.8.5
torch 1.7.0
torchvision 0.8.0
gco-wrapper (https://github.com/Borda/pyGCO)

If you have any problems, please feel free to contact us. Thanks for your attention.