Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Requirements

To launch the training please run train.py. The hyperparameters can be updated in def main function as a dictionary.
For faster convergence, please pretrain the attention module for the domain whose segmenation labels are available, by running python train_segmentation.py attention_mr
For training the upper bount U-Net on MRI modality, use the following command - python train_segmentation.py mr
To evaluate the trained model, please run python run_evaluation.py sasan ct for evaluating the performance of MRI to CT domain adaptation. For the other direction CT to MRI, run python run_evaluation.py sasan mr.

Link to our pre-trained models on Whole Heart Multimodal dataset and code.
Link to Whole Heart Multimodal dataset pre-processed training tf_record files can be found here. The test mr data is available here and test ct data is available here

To convert the tf_records training data to .npy format please use the script convert_tfrecords.py <modality>, where <modality> is either mr or ct.