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Automatic Segmentation of the Spinal Cord Using Continuous Max Flow with Cross-sectional Similarity Prior and Tubularity Features

  • Simon PezoldEmail author
  • Ketut Fundana
  • Michael Amann
  • Michaela Andelova
  • Armanda Pfister
  • Till Sprenger
  • Philippe C. Cattin
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)

Abstract

Segmenting tubular structures from medical image data is a common problem; be it vessels, airways, or nervous tissue like the spinal cord. Many application-specific segmentation techniques have been proposed in the literature, but only few of them are fully automatic and even fewer approaches maintain a convex formulation. In this paper, we show how to integrate a cross-sectional similarity prior into the convex continuous max-flow framework that helps to guide segmentations in image regions suffering from noise or artefacts. Furthermore, we propose a scheme to explicitly include tubularity features in the segmentation process for increased robustness and measurement repeatability. We demonstrate the performance of our approach by automatically segmenting the cervical spinal cord in magnetic resonance images, by reconstructing its surface, and acquiring volume measurements.

Keywords

Spinal Cord Segmentation Result Tubular Structure Surface Reconstruction Dice Coefficient 
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.

Notes

Acknowledgments

We would like to thank Ernst-Wilhelm Radue and the MIAC AG, University Hospital Basel, Basel, Switzerland, for their support.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Simon Pezold
    • 1
    Email author
  • Ketut Fundana
    • 1
  • Michael Amann
    • 2
  • Michaela Andelova
    • 2
  • Armanda Pfister
    • 2
  • Till Sprenger
    • 2
  • Philippe C. Cattin
    • 1
  1. 1.Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
  2. 2.University Hospital BaselBaselSwitzerland

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