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Max Fusion U-Net (MFU-Net) for multi-modal cardiac anatomy and pathology segmentation

Implementation of the MFU-Net model to perform multi-modal caridac anatomy and pathology segmentation. For further details please see our [paper], accepted in [MICCAI-2020 Workshop: MyoPS-20].

Python dependencies to run the code is listed in the file 'requirements.txt'.

The structure of this project is the following:

  • configuration: package containing configuration parameters for running an experiment.
  • layers: package with custom Keras layers
  • loaders: package with data loaders
  • models: package with the SDNet model and other Keras models
  • model_executors: package with scripts for running an experiment
  • callbacks: package with Keras callbacks for printing images and losses during training
  • DataProcess: package with some of the data preprocess codes for some public datasets

To define a new data loader, extend class base_loader.Loader, and register the loader in The datapath is specified in

To run an experiment, execute, passing the configuration filename, the split number as runtime parameters, and the testmode:

 --config unet_multi_modal_cardiac --split 0 --testmode feature-concat-attention-maxfuseall-keeporg

The test mode is defined as follows: pixel-concat: multi-modal information is merged by pixel-concatenation feature-concat: multi-modal information is merged by feature-concatenation maxfuseall: multi-modal information is merged by maximum operator at all the encoder-decoder skipping connections; keeporg: the original concatenated features across different modalities are kept in the skipping connections; attention: dedicate a spatial attention module at the end of the decoder

Details can be referred to the paper and the ./models/ In addition, other module instructions can be referred to from the shell-scripts in the folder: 64-TrainingScriptPaper-MaxFuse-NoPretrained TrainingScriptsChallenge

To run an test, execute as follows:

--test True --config unet_multi_modal_cardiac --split 0 --testmode feature-concat-attention-maxfuseall-keeporg --test True


If you use this code for your research, please cite our paper:

  title={Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling},
  author={Haochuan, Jiang and Wang, Chengjia and Chartsias, Agisilaos and Tsaftaris, Sotirios A},
  booktitle={Myocardial pathology segmentation combining multi-sequence SMR - MyoPS 2020},


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