Sheet-like white matter fiber tracts: representation, clustering, and quantitative analysis

Authors:
Mahnaz Maddah;James V. Miller;Edith V. Sullivan;Adolf Pfefferbaum;Torsten Rohlfing
Affiliations:
Neuroscience Program, SRI International, Menlo Park, CA;Interventional and Therapy, GE Global Research, Niskayuna, NY;Neuroscience Program, SRI International, Menlo Park, CA and Dept. of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA;Neuroscience Program, SRI International, Menlo Park, CA and Dept. of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA;Neuroscience Program, SRI International, Menlo Park, CA
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2011

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Abstract

We introduce an automated and probabilistic method for subjectspecific segmentation of sheet-like fiber tracts. In addition to clustering of trajectories into anatomically meaningful bundles, the method provides statistics of diffusion measures by establishing point correspondences on the estimated medial representation of each bundle. We also introduce a new approach for medial surface generation of sheet-like fiber bundles in order too initialize the proposed clustering algorithm. Applying the new method to a population study of brain aging on 24 subjects demonstrates the capabilities and strengths of the algorithm in identifying and visualizing spatial patterns of group differences.