Multi-dimensional Gated Recurrent Units

This repository contains the code used to generate the result in the paper Automated Segmentation of Multiple Sclerosis Lesions using Multi-Dimensional Gated Recurrent Units. It is implemented in Python using the deep learning libraries PyTorch and TensorFlow each, modified versions were also used to reach 1st place in the ISBI 2015 longitudinal lesion segmentation challenge, 2nd place in the white matter hyperintensities challenge of MICCAI 2017 (and its previous implementation using Caffe made 3rd place in the MrBrainS13 Segmentation challenge). It was also applied in the BraTS 2017 competition, where the information on the exact rank are still unknown.

Since being published the first time using a Caffe implementation, the code has been improved on quite a bit, especially to facilitate handling training and testing runs. The reported results should still be reproducible though using this new implementation with TensorFlow and PyTorch. (The former Caffe code is not maintained anymore (there are probably breaking changes in CuDNN, not tested), but a snapshot of it is included in this release in the folder tensorflow_extra_ops as additional operation for TensorFlow.)

The code has been developed in Python==3.5.2. It is best to set up a virtual environment (e.g. with conda) with the mentioned properties in order to develop the deep learning model. For this purpose, follow the instructions in the docs, and install mdgru (together with mvloader) using pip. In addition, make sure you have CUDA/cuDNN installed.

pip install git+https://github.com/zubata88/mdgru.git
pip install git+https://github.com/spezold/mvloader.git

Papers

Reference implementation (and based on former Caffe version):

@inproceedings{andermatt2016multi,
  title={Multi-dimensional gated recurrent units for the segmentation of biomedical 3D-data},
  author={Andermatt, Simon and Pezold, Simon and Cattin, Philippe},
  booktitle={International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis},
  pages={142--151},
  year={2016},
  organization={Springer}
}

Code also used for (with modifications):

@inproceedings{andermatt2017a,
  title = {{{Automated Segmentation of Multiple Sclerosis Lesions}} using {{Multi-Dimensional Gated Recurrent Units}}},
  timestamp = {2017-08-09T07:27:10Z},
  journal = {Lecture Notes in Computer Science},
  author = {Andermatt, Simon and Pezold, Simon and Cattin, Philippe},
  year = {2017},
  booktitle={International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
  note = {{{[accepted]}}},
  organization={Springer}
}

@article{andermatt2017b,
  title={Multi-dimensional Gated Recurrent Units for Automated Anatomical Landmark Localization},
  author={Andermatt, Simon and Pezold, Simon and Amann, Michael and Cattin, Philippe C},
  journal={arXiv preprint arXiv:1708.02766},
  year={2017}
}

@article{andermatt2017wmh,
  title={Multi-dimensional Gated Recurrent Units for the Segmentation of White Matter Hyperintensites},
  author={Andermatt, Simon and Pezold, Simon and Cattin, Philippe}
}

@inproceedings{andermatt2017brats,
title = {Multi-dimensional Gated Recurrent Units for
Brain Tumor Segmentation},
author = {Simon Andermatt and Simon Pezold and Philippe C. Cattin},
year = 2017,
booktitle = {2017 International {{MICCAI}} BraTS Challenge}
}

When using this code, please cite at least andermatt2016multi, since it is the foundation of this work. Furthermore, feel free to cite the publication matching your use-case from above. E.g. if you're using the code for pathology segmentation, it would be adequate to cite andermatt2017a as well.

Acknowledgements

We thank the Medical Image Analysis Center for funding this work.