Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer

Paper

This is the official implementation of the paper:

Semi-supervised-Cardiac-Image-Segmentation-via-Label-Propagation-and-Style-Transfer,

which won the 2nd place in MICCAI 2020 Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms)

Result

Usage

Please change the input and output paths before running the code!!!

Please change the input and output paths before running the code!!!

Please change the input and output paths before running the code!!!

• Prepare data

  • Download the data from MICCAI 2020 M&Ms Challenge.

  • Rename the data files. In Data_Proprocess, run python rename.py and python rename_unlabeleddata.py.

• Propagate the label to unlabelled volumes by registration. In Data_Proprocess, run sh register.sh.

• Train a preliminary model with both labelled and unlabelled data.

  • Preprocess the data referring to the usage of nnUNet.

  • In nnunet_semi_sup, train the model by python run/run_trainin.py 3d_fullres nnUNetTrainerV2 TASK_ID fold=all.

• Transfer the style of data by histogram matching. In Data_Proprocess, run python match_to_target_histogram.py.

• Finetune the model with with both original and trasferred data.

  • Preprocess the data referring to the usage of nnUNet.

  • Copy the preliminary model to the finetune model path.

  • In nnunet_semi_sup, finetune the model by python run/run_trainin.py 3d_fullres nnUNetTrainerV2Finetune TASK_ID -f all -c.

• inference on the test data by python inference/predict_simple.py -i INPUT_PATH -o OUTPUT_PATH -t TASK_ID -f all -tr nnUNetTrainerV2Finetune

Citation

If you find this code and paper useful for your research, please kindly cite our paper.

@inproceedings{zhang2020semi,
  title={Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer},
  author={Zhang, Yao and Yang, Jiawei and Hou, Feng and Liu, Yang and Wang, Yixin and Tian, Jiang and Zhong, Cheng and Zhang, Yang and He, Zhiqiang},
  booktitle={International Workshop on Statistical Atlases and Computational Models of the Heart},
  pages={219--227},
  year={2020},
  organization={Springer}
}

Acknowledgement

The implementation is based on the out-of-box nnUNet.