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Learning to Segment 3D Linear Structures Using Only 2D Annotations

  • Mateusz KozińskiEmail author
  • Agata Mosinska
  • Mathieu Salzmann
  • Pascal Fua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

We propose a loss function for training a Deep Neural Network (DNN) to segment volumetric data, that accommodates ground truth annotations of 2D projections of the training volumes, instead of annotations of the 3D volumes themselves. In consequence, we significantly decrease the amount of annotations needed for a given training set. We apply the proposed loss to train DNNs for segmentation of vascular and neural networks in microscopy images and demonstrate only a marginal accuracy loss associated to the significant reduction of the annotation effort. The lower labor cost of deploying DNNs, brought in by our method, can contribute to a wide adoption of these techniques for analysis of 3D images of linear structures.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mateusz Koziński
    • 1
    Email author
  • Agata Mosinska
    • 1
  • Mathieu Salzmann
    • 1
  • Pascal Fua
    • 1
  1. 1.Computer Vision LaboratoryÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland

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