Model-based segmentation of hippocampal subfields in ultra-high resolution in vivo MRI

Koen Van Leemput, Akram Bakkour, Thomas Benner, Graham Wiggins, Lawrence L. Wald, Jean Augustinack, Bradford C. Dickerson, Polina Golland, Bruce Fischl

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. In this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampal area are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals. © 2008 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention
Number of pages9
Volume5241
PublisherSpringer-verlag Berlin
Publication date2008
Pages235-243
ISBN (Print)978-3-540-85987-1
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event11th International Conference on Medical Image Computing and Computer Assisted Intervention - New York University, New York, NY, United States
Duration: 6 Sep 200810 Sep 2008
Conference number: 11
http://miccai2008.rutgers.edu/

Conference

Conference11th International Conference on Medical Image Computing and Computer Assisted Intervention
Number11
LocationNew York University
CountryUnited States
CityNew York, NY
Period06/09/200810/09/2008
Internet address
SeriesLecture Notes in Computer Science
ISSN0302-9743

Keywords

  • Bayesian networks
  • Computer science
  • Image segmentation
  • Security of data
  • Technology transfer
  • Medical computing

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