Tractography Segmentation Using a Hierarchical Dirichlet Processes Mixture Model

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 learnt from data with a Dirichlet process (DP) prior instead of being manually specified. After the models of bundles have been learnt from training data without supervision, they can be used as priors to cluster/classify fibers of new 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 without subsampling. We present results on multiple data sets, the largest of which has more than 120, 000 fibers.

• Type: Conference
• Year: 2009
• Where: IPMI
• Authors: Carl-Fredrik Westin, W. Eric L. Grimson, Xiaogang Wang
• Keywords: Hierarchical Dirichlet Processes, IPMI 2009, Medical Imaging, Nonparametric Bayesian Framework, Training Data