Mixed-Effects shape models for estimating longitudinal changes in anatomy

Authors:
Manasi Datar;Prasanna Muralidharan;Abhishek Kumar;Sylvain Gouttard;Joseph Piven;Guido Gerig;Ross Whitaker;P. Thomas Fletcher
Affiliations:
Scientific Computing and Imaging Institute, University of Utah;Scientific Computing and Imaging Institute, University of Utah;Department of Computer Science, University of Maryland;Scientific Computing and Imaging Institute, University of Utah;Carolina Institute for Developmental Disabilities, University of North Carolina;Scientific Computing and Imaging Institute, University of Utah;Scientific Computing and Imaging Institute, University of Utah;Scientific Computing and Imaging Institute, University of Utah
Venue:
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
Year:
2012

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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 T2 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.