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Sheet-like White Matter Fiber Tracts: Representation, Clustering, and Quantitative Analysis

Institution:
1Neuroscience Program, SRI International, Menlo Park, CA, USA.
2Interventional and Therapy, GE Global Research, Niskayuna, NY, USA.
3Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2011
Publication Date:
Sep-2011
Journal:
Med Image Comput Comput Assist Interv
Volume Number:
14
Issue Number:
Pt 2
Pages:
191-9
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2011 Sep;14(Pt 2):191-9.
PubMed ID:
21995029
PMCID:
PMC3287070
Appears in Collections:
NAC, SLICER
Sponsors:
R01 AA005965/AA/NIAAA NIH HHS/United States
R01 AA012388/AA/NIAAA NIH HHS/United States
K05 AA017168/AA/NIAAA NIH HHS/United States
U01 AA017347/AA/NIAAA NIH HHS/United States
R01 EB008381/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
Generated Citation:
Maddah M., Miller J.V., Sullivan E.V., Pfefferbaum A., Rohlfing T. Sheet-like White Matter Fiber Tracts: Representation, Clustering, and Quantitative Analysis. Int Conf Med Image Comput Comput Assist Interv. 2011 Sep;14(Pt 2):191-9. PMID: 21995029. PMCID: PMC3287070.
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We introduce an automated and probabilistic method for subject-specific 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.

Additional Material
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Maddah-MICCAI2011-fig3.jpg (87.085kB)