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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.
This is the version of our TMI paper.
Please refer to the branch SIFA-v1 for the version of our AAAI paper.

Paper

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

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

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