robinsandkuehler / mdgru

Code for Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical Data

Multi-dimensional Gated Recurrent Units

This repository contains the code used to produce the results in the papers Automated Segmentation of Multiple Sclerosis Lesions using Multi-Dimensional Gated Recurrent Units and Multi-dimensional Gated Recurrent Units for Automated Anatomical Landmark Localization. It was 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 in CAFFE made 3rd place in the MrBrainS13 Segmentation challenge. We also competed in BraTS 2017, where the information on the exact rank are still unknown. This release is implemented using the tensorflow framework. The 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. Since being published, 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 implementation.

The latest full documentation is available here.

Papers

Reference implementation for - and based on - 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 used for:

@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.