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Low-Rank to the Rescue – Atlas-Based Analyses in the Presence of Pathologies

  • Xiaoxiao Liu
  • Marc Niethammer
  • Roland Kwitt
  • Matthew McCormick
  • Stephen Aylward
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Low-rank image decomposition has the potential to address a broad range of challenges that routinely occur in clinical practice. Its novelty and utility in the context of atlas-based analysis stems from its ability to handle images containing large pathologies and large deformations. Potential applications include atlas-based tissue segmentation and unbiased atlas building from data containing pathologies. In this paper we present atlas-based tissue segmentation of MRI from patients with large pathologies. Specifically, a healthy brain atlas is registered with the low-rank components from the input MRIs, the low-rank components are then re-computed based on those registrations, and the process is then iteratively repeated. Preliminary evaluations are conducted using the brain tumor segmentation challenge data (BRATS ’12).

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaoxiao Liu
    • 1
  • Marc Niethammer
    • 2
  • Roland Kwitt
    • 3
  • Matthew McCormick
    • 1
  • Stephen Aylward
    • 1
  1. 1.Kitware Inc.USA
  2. 2.University of North Carolina at Chapel HillUSA
  3. 3.Department of Computer ScienceUniversity of SalzburgAustria

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