Segmentation of Short Association Bundles in Massive Tractography Datasets Using a Multi-subject Bundle Atlas [chapter]

Pamela Guevara, Delphine Duclap, Cyril Poupon, Linda Marrakchi-Kacem, Josselin Houenou, Marion Leboyer, Jean-François Mangin
2011 Lecture Notes in Computer Science  
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
more » ... f 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.
doi:10.1007/978-3-642-25085-9_83 fatcat:ygne5rvcnvebja4lqyjnixxsp4