Multimodal segmentation with disentangled representations

Implementations of the MMSDNet and DAFNet models that perform multimodal image segmentation using a disentangled representation of anatomy and modality factors. For further details please see our paper Multimodal Cardiac Segmentation Using Disentangled Representation Learning presented in STACOM 2019, and the pre-print [Disentangle align and fuse for multimodal and semi-supervised image segmentation].

Pseudocode of the training process is uploaded on pseudocode.pdf

The code is written in Python 3.6 with Keras 2.1.6 and tensorflow 1.4.0 and experiments were run in Titan-X and Titan-V GPUs. The requirements.txt file contains all Python library versions.

This project is structured in different packages as follows:

• callbacks: contains Keras callbacks for printing images and losses during training
• configuration: contains configuration files for running an experiment
• layers: contains custom Keras layers, e.g. the STN layer
• loaders: contains definitions of data loaders
• model_components: contains implementations of individual components, e.g. the encoders and decoders
• model_executors: contains code for loading data and training models
• models: contains Keras implementations of MMSDNet, DAFNet
• utils: package with utility functions used in the project

To define a new data loader, extend class base_loader.Loader, and register the loader in loader_factory.py. The datapaths are specified in base_loader.py.

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

python experiment.py --config myconfiguration --split 0

Optional parameters include the proportion of labels in modality 2 data for unsupervised learning, e.g.

python experiment.py --config myconfiguration --split 0 --l_mix 0.5

Sample config files for MMSDNet and DAFNet are placed in the configuration package for CHAOS data.

Citation

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

@InProceedings{chartsias2020multimodal,
author="Chartsias, Agisilaos and Papanastasiou, Giorgos and Wang, Chengjia and Stirrat, Colin and Semple, Scott and Newby, David and Dharmakumar, Rohan and Tsaftaris, Sotirios A.",
title="Multimodal Cardiac Segmentation Using Disentangled Representation Learning",
booktitle="Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="128--137",
isbn="978-3-030-39074-7"
}

@article{chartsias2020disentangle,
  title={Disentangle, align and fuse for multimodal and semi-supervised image segmentation},
  author={Chartsias, Agisilaos and Papanastasiou, Giorgos and Wang, Chengjia and Semple, Scott and Newby, David and Dharmakumar, Rohan and Tsaftaris, Sotirios A},
  journal={arXiv preprint arXiv:1911.04417},
  year={2019}
}