title: Tractography segmentation using a hierarchical Dirichlet processes mixture model.
abstract: In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. The number of clusters is automatically learned driven by data with a Dirichlet process (DP) prior instead of being manually specified. After the models of bundles have been learned from training data without supervision, they can be used as priors to cluster/classify fibers of new subjects for comparison across subjects. When clustering fibers of new subjects, new clusters can be created for structures not observed in the training data. Our approach does not require computing pairwise distances between fibers and can cluster a huge set of fibers across multiple subjects. We present results on several data sets, the largest of which has more than 120,000 fibers.
authors.affiliation: Department of Electronic Engineering, the Chinese University of Hong Kong, Hong Kong. xgwang@ee.cuhk.edu.hk
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wang
authors.forename: W Eric L
authors.initials: WE
authors.lastname: Grimson
authors.forename: Carl-Fredrik
authors.initials: CF
authors.lastname: Westin
mesh_terms: D000465:Algorithms; D001933:Brain Stem / Q000033:anatomy & histology / Q000502:physiology / Q000648:ultrastructure; D002531:Cerebellum / Q000033:anatomy & histology / Q000502:physiology / Q000648:ultrastructure; D003196:Computer Graphics; D056324:Diffusion Tensor Imaging / Q000379:methods; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008959:Models, Neurological; D009412:Nerve Fibers / Q000502:physiology* / Q000648:ultrastructure*; D011336:Probability; D018709:Statistics, Nonparametric