Evaluation of brain image registration methods
Speaker: Arno Klein , Columbia University
Establishing correspondences across brains for the purposes of comparison and group analysis is almost universally done by registering images to one another either directly or via a template. However, there are many registration algorithms to choose from. The first part of this talk will give an overview of a recent evaluation study comparing fully automated nonlinear deformation methods applied to brain image registration (Klein et al. 2009). This study was restricted to volume-based methods, and an ongoing extension of this study is the first known to the authors that directly compares some of the most accurate of these methods with surface-based registration methods, as well as the first study to compare registrations of whole-head and de-skulled brain images. More than 6,000 registrations between 40 manually labeled brain images have been performed so far by the volume-based algorithms ART and SyN and the surface-based algorithms FreeSurfer and Spherical Demons. We used permutation tests and indifference-zone ranking to compare the overlap performance for eight scenarios: ART and SyN on brain images with and without skulls, SyN, FreeSurfer, and Spherical Demons via custom templates, and FreeSurfer via its default atlas.