Shallow Fully-Connected Neural Networks for Ischemic Stroke-Lesion Segmentation in MRI

  • Christian Lucas
  • Oskar Maier
  • Mattias P. Heinrich
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Automatic image segmentation of stroke lesions could be of great importance for aiding the treatment decision. Convolutional neural networks obtain high accuracy for this task at the cost of prohibitive computational demand for time-sensitive clinical scenarios. In this work, we study the use of classical fully-connected neural networks (FC-NN) based on hand-crafted features, which achieve much shorter runtimes. We show that recent advances in optimization and regularization of deep learning can be successfully transferred to FC-NNs to improve the training process and achieve comparable accuracy to random decision forests.

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Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  • Christian Lucas
    • 1
    • 2
  • Oskar Maier
    • 1
    • 2
  • Mattias P. Heinrich
    • 1
  1. 1.Institute of Medical InformaticsUniversity of LuebeckErlangen-NürnbergDeutschland
  2. 2.Graduate School for Computing in Medicine and Life SciencesUniversity of LuebeckErlangen-NürnbergDeutschland

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