Segmentation of Short Association Bundles in Massive Tractography Datasets Using a Multi-subject Bundle Atlas - Guevara, Pamela; Duclap, Delphine; Poupon, Cyril; Marrakchi-Kacem, Linda; Houenou, Josselin; Leboyer, Marion; Mangin, Jean-François (Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2011)
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.