Attention-enhanced Disentangled Representation Learning for Unsupervised Domain Adaptation in Cardiac Segmentation (accepted by MICCAI 2022)

Tensorflow implementation of our unsupervised domain adaptation cardiac segmentation framework.

Requirements

• Tensorflow 1.14.0
• python 3.6
• CPU or GPU + CUDA CuDNN

Dataset

• The dataset used is the same as SIFA. The training data can be downloaded here. The testing CT data can be downloaded here. The testing MR data can be downloaded here.
• Put 'tfrecords' training data and 'npz' test data of two domains into corresponding folders under ./data accordingly.
• Run './create_datalist.py' to generate the datalists containing the path of each data.
• Run './convertToNpz.py' to convert the 'nii.gz' file to 'npz' file.

Train

• Run './readCkpt.py' to get the initial model of coarse alignment in our model.
• Run './main.py' to start the training process

Evaluate

• Our trained models can be downloaded from here.
• Run './evaluate.py' to start the evaluation.

Acknowledgement

This code is heavily borrowed from SIFA, partly from DGNet.

Citation

If you use this code for your research, please cite our papers.