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Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy

  • Manasi Datar
  • Prasanna Muralidharan
  • Abhishek Kumar
  • Sylvain Gouttard
  • Joseph Piven
  • Guido Gerig
  • Ross Whitaker
  • P. Thomas Fletcher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7570)

Abstract

In this paper, we propose a new method for longitudinal shape analysis that fits a linear mixed-effects model, while simultaneously optimizing correspondences on a set of anatomical shapes. Shape changes are modeled in a hierarchical fashion, with the global population trend as a fixed effect and individual trends as random effects. The statistical significance of the estimated trends are evaluated using specifically designed permutation tests. We also develop a permutation test based on the Hotelling T 2 statistic to compare the average shapes trends between two populations. We demonstrate the benefits of our method on a synthetic example of longitudinal tori and data from a developmental neuroimaging study.

Keywords

Autism Spectrum Disorder Permutation Test Longitudinal Change Simple Linear Regression Model Anatomical Shape 
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 2012

Authors and Affiliations

  • Manasi Datar
    • 1
  • Prasanna Muralidharan
    • 1
  • Abhishek Kumar
    • 2
  • Sylvain Gouttard
    • 1
  • Joseph Piven
    • 3
  • Guido Gerig
    • 1
  • Ross Whitaker
    • 1
  • P. Thomas Fletcher
    • 1
  1. 1.Scientific Computing and Imaging InstituteUniversity of UtahUSA
  2. 2.Department of Computer ScienceUniversity of MarylandUSA
  3. 3.Carolina Institute for Developmental DisabilitiesUniversity of North CarolinaUSA

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