14 articles 

inria-00624196, version 1

Hippocampal Morphometry Study by Automated Surface Fluid Registration and its Application to Alzheimer's Disease

Jie Shi 1, Yuting Wang 2, Paul M. Thompson 3, Yalin Wang 1

Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability (2011) 170-181

  • 1:  School of Computing, Informatics and Decision Systems Engineering
  • http://engineering.asu.edu/cidse
    Arizona State University Schools of Engineering P.O. Box 9309 | Arizona State University Tempe, AZ 85287-9309 United States
  • 2:  Department of Mathematics [UCLA]
  • http://www.math.ucla.edu/
    University of California, Los Angeles UCLA Mathematics Department Box 951555 Los Angeles, CA 90095-1555 United States
  • 3:  Laboratory of Neuro Imaging [Los Angeles] (LONI)
  • http://www.loni.ucla.edu/
    University of California, Los Angeles Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA United States

Bibliographic reference

  • Type of document: Peer-reviewed conferences/proceedings
  • Domain: Computer Science/Other
  • Title: Hippocampal Morphometry Study by Automated Surface Fluid Registration and its Application to Alzheimer's Disease
  • Abstract: Alzheimer's disease (AD) is a severe and growing public health crisis. Efforts are underway to look for AD early detection in an efficient manner. Among all the AD biomarkers, hippocampal atrophy assessed on high-resolution T1-weighted MRI is the best established and validated. Hippocampal morphometry is increasingly used in the AD research, with modeling the hippocampus as a 3D parametric surface mesh. However, a major question in the analysis is how to align corresponding surface regions across subjects. Here we develop a system for detecting AD symptoms on hippocampal surfaces with an automated surface fluid registration method, which is based on conformal surface representation and mutual information regularized image fluid registration. Since conformal mappings are diffeomorphic and the mutual information method is able to drive a diffeomorphic flow that is adjusted to enforce appropriate surface correspondences in the surface parameter domain, combining conformal and fluid mappings will generate 3D shape correspondences that are diffeomorphic. We also incorporate in the system a novel method to compute curvatures using surface conformal parameterization. Experimental results in three hippocampal datasets show that the new system outperformed an early similar method and the popular SPHARM tool.
  • Full text language: English
  • Publication date: 2011-09-22
  • Audience: international
  • Conference title: Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability
  • Conference city: Toronto
  • Country: Canada
  • Conference date: 2011-09-22
  • Conference date (end): 2011-09-22
  • Scientific editor(s): Pennec, Xavier and Joshi, Sarang and Nielsen, Mads
  • Pagination: 170-181
  • Collaboration(s): Session : Poster

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  • inria-00624196, version 1
  • http://hal.inria.fr/inria-00624196
  • oai:hal.inria.fr:inria-00624196
  • From: Xavier Pennec <>
  • Submitted on: Friday, 16 September 2011 09:40:35
  • Updated on: Friday, 16 September 2011 10:02:22