title: Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy.
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 HotellingT2statistic 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.
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Manasi
authors.initials: M
authors.lastname: Datar
authors.affiliation: Department of Computer Science, University of Maryland.
authors.forename: Prasanna
authors.initials: P
authors.lastname: Muralidharan
authors.affiliation: Carolina Institute for Developmental Disabilities, University of North Carolina.
authors.forename: Abhishek
authors.initials: A
authors.lastname: Kumar
authors.forename: Sylvain
authors.initials: S
authors.lastname: Gouttard
authors.forename: Joseph
authors.initials: J
authors.lastname: Piven
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.forename: Ross
authors.initials: R
authors.lastname: Whitaker
authors.forename: P Thomas
authors.initials: PT
authors.lastname: Fletcher