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.
Type de document :
Article dans une revue
NeuroImage, Elsevier, 2012, epub ahead of print. 〈10.1016/j.neuroimage.2012.02.071〉
Liste complète des métadonnées

https://hal.inria.fr/hal-00700800
Contributeur : Pierre Fillard <>
Soumis le : mercredi 23 mai 2012 - 23:46:32
Dernière modification le : vendredi 24 novembre 2017 - 16:16:01

Identifiants

Collections

CEA | INRIA | DSV

Citation

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, epub ahead of print. 〈10.1016/j.neuroimage.2012.02.071〉. 〈hal-00700800〉

Partager

Métriques

Consultations de la notice

205