Abstract #1032
Connectivity based segmentation of the Corpus Callosum using a novel data mining approach
Gowtham Atluri 1 , An Wu 2 , Essa Yacoub 2 , Kamil Ugurbil 2 , Vipin Kumar 1 , and Christophe Lenglet 2
1
Computer Science and Engineering, University
of Minnesota, Minneapolis, Minnesota, United States,
2
Center
for Magnetic Resonance Research, University of
Minnesota, Minneapolis, Minnesota, United States
Existing approaches that are used to do finer
segmentation of cortical regions using DTI based
tractography data do not make use of the underlying
spatial structure. In this work, we extend a popular
Shared Nearest Neighbor (SNN) based clustering approach
in order to account for spatial structure in the data.
We use this approach to discover finer segmentation of
the Corpus Callosum in 2 normal subjects using
tractography data computed from a 3T and a 7T DTI scan.
Our results suggest that our new approach results in a
segmentation that is consistent between 3T and 7T.
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