• P Guevara, D Duclap, C Poupon, L Marrakchi-Kacem, P Fillard, D Le Bihan, M Leboyer, J Houenou, and J-F Mangin.
• I(2)BM, CEA, Gif-sur-Yvette, France. pamela.guevara@gmail.com
• Neuroimage. 2012 Jul 16; 61 (4): 1083-99.

AbstractThis paper presents a method for automatic segmentation of white matter 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. This atlas is a model of the brain white matter organization, computed for a group of subjects, made up of a set of generic fiber bundles that can be detected in most of the population. Each atlas bundle corresponds to several inter-subject clusters manually labeled to account for subdivisions of the underlying pathways often presenting large variability across subjects. 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. The atlas, composed of 36 known deep white matter bundles and 47 superficial white matter bundles in each hemisphere, was inferred from a first database of 12 brains. It was successfully used to segment the deep white matter bundles in a second database of 20 brains and most of the superficial white matter bundles in 10 subjects of the same database.Copyright © 2012 Elsevier Inc. All rights reserved.

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