Tractography segmentation using a hierarchical Dirichlet processes mixture model
Name
nihms226973.pdf
Description
Accepted version
Size
2.45 MB
Format
Adobe PDF
Checksum (MD5)
8908bb1d02b6634488f7159eb89c6cf2
Author(s)
Wang, Xiaogang
Grimson, W. Eric L.
Westin, Carl-Fredrik
Date Issued
August 2010
Journal
NeuroImage
Publisher
Elsevier BV
Citation
Wang, Xiaogang et al., "Tractography segmentation using a hierarchical Dirichlet processes mixture model." NeuroImage 54, 1 (January 2011): 290-302 doi. 10.1016/j.neuroimage.2010.07.050 ©2010 Authors
Version
Author's final manuscript
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. ©2010 Elsevier Inc.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.neuroimage.2010.07.050
