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.

[1]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[2]  W. Eric L. Grimson,et al.  Trajectory analysis and semantic region modeling using a nonparametric Bayesian model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[4]  Alfred O. Hero,et al.  Fiber Tract Clustering on Manifolds With Dual Rooted-Graphs , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  R. Deriche,et al.  Simultaneous Manifold Learning and Clustering: Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas , 2008, The MIDAS Journal.

[6]  T. Ferguson.  A Bayesian Analysis of Some Nonparametric Problems , 1973 .

[7]  W. Eric L. Grimson,et al.  Automated Atlas-Based Clustering of White Matter Fiber Tracts from DTMRI , 2005, MICCAI.

[8]  Michael I. Jordan,et al.  Hierarchical Dirichlet Processes , 2006 .

[9]  Padhraic Smyth,et al.  Hierarchical Dirichlet Processes with Random Effects , 2006, NIPS.

[10]  J. Thiran,et al.  Fiber tracts of high angular resolution diffusion MRI are easily segmented with spectral clustering. , 2005 .

[11]  Mark W. Woolrich,et al.  Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models , 2009, NeuroImage.

[12]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.

[13]  Xiaogang Wang,et al.  Learning motion patterns using hierarchical Bayesian models , 2009 .

[14]  Carl-Fredrik Westin,et al.  Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas , 2007, IEEE Transactions on Medical Imaging.

[15]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[16]  John D. E. Gabrieli,et al.  Knowledge-Based Classification of Neuronal Fibers in Entire Brain , 2005, MICCAI.

[17]  École d'été de probabilités de Saint-Flour,et al.  École d'été de probabilités de Saint-Flour XIII - 1983 , 1985 .

[18]  W. Eric L. Grimson,et al.  A unified framework for clustering and quantitative analysis of white matter fiber tracts , 2008, Medical Image Anal..

[19]  J. Sethuraman.  A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS , 1991 .

[20]  Max Welling,et al.  Asynchronous Distributed Learning of Topic Models , 2008, NIPS.

[21]  Peter Savadjiev,et al.  Streamline Flows for White Matter Fibre Pathway Segmentation in Diffusion MRI , 2008, MICCAI.

[22]  Rachid Deriche,et al.  Bayesian framework for white matter fibers similarity measure , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[23]  A. Anderson,et al.  Classification and quantification of neuronal fiber pathways using diffusion tensor MRI , 2003, Magnetic resonance in medicine.

[24]  I. Corouge,et al.  Analysis of brain white matter via fiber tract modeling , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Rachid Deriche,et al.  Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers , 2010, NeuroImage.

[26]  Carl-Fredrik Westin,et al.  Clustering Fiber Traces Using Normalized Cuts , 2004, MICCAI.

[27]  P. Hagmann,et al.  Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[28]  W. Eric L. Grimson,et al.  Modeling of anatomical information in clustering of white matter fiber trajectories using Dirichlet distribution , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[29]  D. Tuch.  Q‐ball imaging , 2004, Magnetic resonance in medicine.

[30]  Adelino R. Ferreira da Silva,et al.  A Dirichlet process mixture model for brain MRI tissue classification , 2007, Medical Image Anal..

[31]  D. Aldous.  Exchangeability and related topics , 1985 .

[32]  Anna Vilanova,et al.  Evaluation of fiber clustering methods for diffusion tensor imaging , 2005, VIS 05. IEEE Visualization, 2005..

[33]  Nikos Komodakis,et al.  Clustering of the Human Skeletal Muscle Fibers Using Linear Programming and Angular Hilbertian Metrics , 2009, IPMI.

[34]  N. Makris,et al.  High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity , 2002, Magnetic resonance in medicine.

[35]  W. Eric L. Grimson,et al.  Efficient Population Registration of 3D Data , 2005, CVBIA.

[36]  Jean-Francois Mangin,et al.  High Level Group Analysis of FMRI Data Based on Dirichlet Process Mixture Models , 2007, IPMI.

[37]  Michael I. Jordan,et al.  Variational inference for Dirichlet process mixtures , 2006 .