Ressources.ircam

Group studies in neuroimaging raise the need for statistical methods to find (in)variants in large data sets of anatomical structures. In this talk, we will present a generic statistical framework that can deal with 3D structural images as well as shapes segmented from images such as white matter fiber tracts, meshes of the cortical structures, sulcal ribbons, etc. The mean is given as a typical anatomical configuration that captures the geometric invariants within the studied population. The variance is described by typical deformations of the mean configuration. The framework relies on the metric of large diffeomorphic deformations in an adaptive finite-dimensional setting. Extension of this framework for the analysis of longitudinal shape data sets will be also presented. The talk will be illustrated by various examples taken from neuroanatomical studies.