Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas.

Abstract : This 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.
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https://hal.inria.fr/hal-00700800
Contributor : Pierre Fillard <>
Submitted on : Wednesday, May 23, 2012 - 11:46:32 PM
Last modification on : Monday, February 10, 2020 - 6:13:43 PM

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P. Guevara, D. Duclap, C. Poupon, L. Marrakchi-Kacem, P. Fillard, et al.. Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas.. NeuroImage, Elsevier, 2012, 61 (4), pp.1083-1099. ⟨10.1016/j.neuroimage.2012.02.071⟩. ⟨hal-00700800⟩

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