Segmentation of short association bundles in massive tractography datasets using a multi-subject bundle atlas

Abstract

This paper presents a method for automatic segmentation of some short association fiber bundles from massive dMRI tractography datasets. The method is based on a multi-subject bundle atlas derived from a two-level intra-subject and inter-subject clustering strategy. Each atlas bundle corresponds to one or more inter-subject clusters, presenting similar shapes. An atlas bundle is represented by the multi-subject list of the centroids of all intra-subject clusters in order to get a good sampling of the shape and localization variability. An atlas of 47 bundles is inferred from a first database of 12 brains, and used to segment the same bundles in a second database of 10 brains. © 2011 Springer-Verlag.