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Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

Tensorflow implementation of our unsupervised cross-modality domain adaptation framework. <br/> This is the version of our TMI paper. <br/> Please refer to the branch SIFA-v1 for the version of our AAAI paper. <br/>

Paper

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation <br/> IEEE Transactions on Medical Imaging <br/> <br/>

Installation

• Install TensorFlow 1.10 and CUDA 9.0
• Clone this repo

git clone https://github.com/cchen-cc/SIFA
cd SIFA

Data Preparation

• Raw data needs to be written into tfrecord format to be decoded by ./data_loader.py. The pre-processed data has been released from our work PnP-AdaNet. The training data can be downloaded here. The testing CT data can be downloaded here. The testing MR data can be downloaded here.
• Put tfrecord data of two domains into corresponding folders under ./data accordingly.
• Run ./create_datalist.py to generate the datalists containing the path of each data.

Train

• Modify the data statistics in data_loader.py according to the specifc dataset in use. Note that this is a very important step to correctly convert the data range to [-1, 1] for the network inputs and ensure the performance.
• Modify paramter values in ./config_param.json
• Run ./main.py to start the training process

Evaluate

• Our trained models can be downloaded from Dropbox. Note that the data statistics in evaluate.py need to be changed accordingly as specificed in the script.
• Specify the model path and test file path in ./evaluate.py
• Run ./evaluate.py to start the evaluation.

Citation

If you find the code useful for your research, please cite our paper.

@article{chen2020unsupervised,
  title     = {Unsupervised Bidirectional Cross-Modality Adaptation via 
               Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation},
  author    = {Chen, Cheng and Dou, Qi and Chen, Hao and Qin, Jing and Heng, Pheng Ann},
  journal   = {arXiv preprint arXiv:2002.02255},
  year      = {2020}
}

@inproceedings{chen2019synergistic,
  author    = {Chen, Cheng and Dou, Qi and Chen, Hao and Qin, Jing and Heng, Pheng-Ann},
  title     = {Synergistic Image and Feature Adaptation: 
               Towards Cross-Modality Domain Adaptation for Medical Image Segmentation},
  booktitle = {Proceedings of The Thirty-Third Conference on Artificial Intelligence (AAAI)},
  pages     = {865--872},
  year      = {2019},
}

Acknowledgement

Part of the code is revised from the Tensorflow implementation of CycleGAN.

Note

• The repository is being updated
• Contact: Cheng Chen (chencheng236@gmail.com)