Automated atlas-based clustering of white matter fiber tracts from DTMRI

Authors:
Mahnaz Maddah;Andrea U. J. Mewes;Steven Haker;W. Eric L. Grimson;Simon K. Warfield
Affiliations:
Computer Science and Artificial Intelligence Laboratory, Massachussets Institute of Technology, Cambridge, MA;Computational Radiology Laboratory, Brigham and Women’s Hospital, Boston, MA;Computational Radiology Laboratory, Brigham and Women’s Hospital, Boston, MA;Computer Science and Artificial Intelligence Laboratory, Massachussets Institute of Technology, Cambridge, MA;Computer Science and Artificial Intelligence Laboratory, Massachussets Institute of Technology, Cambridge, MA
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundles of fiber tracts in the human brain is desired. To include the anatomical knowledge in the clustering, we invoke an atlas of fiber tracts, labeled by the number of bundles of interest. In this work, we construct such an atlas and use it to cluster all fiber tracts in the white matter. To build the atlas, we start with a set of labeled ROIs specified by an expert and extract the fiber tracts initiating from each ROI. Affine registration is used to project the extracted fiber tracts of each subject to the atlas, whereas their B-spline representation is used to efficiently compare them to the fiber tracts in the atlas and assign cluster labels. Expert visual inspection of the result confirms that the proposed method is very promising and efficient in clustering of the known bundles of fiber tracts.