Belief propagation based segmentation of white matter tracts in DTI

Pierre Louis Bazin, John Bogovic, Daniel Reich, Jerry L. Prince, Dzung L. Pham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a belief propagation approach to the segmentation of the major white matter tracts in diffusion tensor images of the human brain. Unlike tractography methods that sample multiple fibers to be bundled together, we define a Markov field directly on the diffusion tensors to separate the main fiber tracts at the voxel level. A prior model of shape and direction guides a full segmentation of the brain into known fiber tracts; additional, unspecified fibers; and isotropic regions. The method is evaluated on various data sets from an atlasing project, healthy subjects, and multiple sclerosis patients.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings
Pages943-950
Number of pages8
EditionPART 1
DOIs
StatePublished - Dec 1 2009
Event12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 - London, United Kingdom
Duration: Sep 20 2009Sep 24 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5761 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
CountryUnited Kingdom
CityLondon
Period9/20/099/24/09

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Bazin, P. L., Bogovic, J., Reich, D., Prince, J. L., & Pham, D. L. (2009). Belief propagation based segmentation of white matter tracts in DTI. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings (PART 1 ed., pp. 943-950). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5761 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-04268-3_116