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

• Published in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications pp. 701 - 708
• Main Authors: Guevara, Pamela, Duclap, Delphine, Poupon, Cyril, Marrakchi-Kacem, Linda, Houenou, Josselin, Leboyer, Marion, Mangin, Jean-François
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2011
• Series: Lecture Notes in Computer Science
• Subjects: Dirichlet Process Mixture Model; Orientation Distribution Function; Right Hemisphere; Talairach Space; White Matter Bundle
• ISBN: 364225084X; 9783642250842
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-25085-9_83

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.