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Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data

  • Simon Andermatt
  • Simon Pezold
  • Philippe Cattin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10008)

Abstract

We present a supervised deep learning method to automatically segment 3D volumes of biomedical image data. The presented method takes advantage of a neural network with the main layers consisting of multi-dimensional gated recurrent units. We apply an on-the-fly data augmentation technique which allows for accurate estimations without the need for either a huge amount of training data or advanced data pre- or postprocessing. We show that our method performs amongst the leading techniques on a popular brain segmentation challenge dataset in terms of speed, accuracy and memory efficiency. We describe in detail advantages over a similar method which uses the well-established long short-term memory.

Keywords

Deep learning GRU Multi-dimensional RNN Segmentation 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Simon Andermatt
    • 1
  • Simon Pezold
    • 1
  • Philippe Cattin
    • 1
  1. 1.Department of Biomedical EngineeringUniversity of BaselAllschwilSwitzerland

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