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Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation

  • Richard McKinleyEmail author
  • Rik Wepfer
  • Tom Gundersen
  • Franca Wagner
  • Andrew Chan
  • Roland Wiest
  • Mauricio Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

Abstract

Biomedical image segmentation requires both voxel-level information and global context. We report on a deep convolutional architecture which combines a fully-convolutional network for local features and an encoder-decoder network in which convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutional layers and tied unpooling. We apply the method to segmenting multiple sclerosis lesions and gliomas.

Keywords

Convolutional Neural Network Multiple Sclerosis Lesion Medical Image Segmentation Convolutional Layer Lesion Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Richard McKinley
    • 1
    Email author
  • Rik Wepfer
    • 1
  • Tom Gundersen
    • 2
  • Franca Wagner
    • 1
  • Andrew Chan
    • 3
  • Roland Wiest
    • 1
  • Mauricio Reyes
    • 4
  1. 1.Department of Diagnostic and Interventional NeuroradiologyInselspital, University of BernBernSwitzerland
  2. 2.Red Hat ABOsloNorway
  3. 3.University Clinic for NeurologyInselspital, University of BernBernSwitzerland
  4. 4.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland

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