Synergistic Image and Feature Adaptation:
Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

Tensorflow implementation of our unsupervised cross-modality domain adaptation framework

Paper

Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
AAAI Conference on Artificial Intelligence, 2019 (oral)

Installation

• Install TensorFlow 1.4 and CUDA 8.0
• 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.
• 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 paramter values in ./config_param.json
• Run ./main.py to start the training process

Evaluate

• 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.

@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},
}

@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}
}

Acknowledgement

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

Note

• The repository is being updated
• Contact: Cheng Chen ([email protected])