Segmentation of Short Association Bundles in Massive Tractography Datasets Using a Multi-subject Bundle Atlas

  • Pamela Guevara
  • Delphine Duclap
  • Cyril Poupon
  • Linda Marrakchi-Kacem
  • Josselin Houenou
  • Marion Leboyer
  • Jean-François Mangin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

This paper presents a method for automatic segmentation of some short association fiber bundles from massive dMRI tractography datasets. The method is based on a multi-subject bundle atlas derived from a two-level intra-subject and inter-subject clustering strategy. Each atlas bundle corresponds to one or more inter-subject clusters, presenting similar shapes. An atlas bundle is represented by the multi-subject list of the centroids of all intra-subject clusters in order to get a good sampling of the shape and localization variability. An atlas of 47 bundles is inferred from a first database of 12 brains, and used to segment the same bundles in a second database of 10 brains.

Keywords

Orientation Distribution Function Right Hemisphere Talairach Space Dirichlet Process Mixture Model White Matter Bundle 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pamela Guevara
    • 1
    • 2
    • 3
  • Delphine Duclap
    • 1
    • 2
  • Cyril Poupon
    • 1
    • 2
  • Linda Marrakchi-Kacem
    • 1
    • 2
  • Josselin Houenou
    • 1
    • 2
    • 4
  • Marion Leboyer
    • 4
  • Jean-François Mangin
    • 1
    • 2
  1. 1.Neurospin, CEAFrance
  2. 2.Institut Fédératif de RechercheFrance
  3. 3.University of ConcepciónConcepciónChile
  4. 4.Department of Psychiatry, INSERM, U955 UnitAP-HP, University Paris-EastFrance

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