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Proceedings Paper

Automated anatomical labeling of the cerebral arteries using belief propagation
Author(s): Murat Bilgel; Snehashis Roy; Aaron Carass; Paul A. Nyquist; Jerry L. Prince
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Paper Abstract

Labeling of cerebral vasculature is important for characterization of anatomical variation, quantification of brain morphology with respect to specific vessels, and inter-subject comparisons of vessel properties and abnormalities. We propose an automated method to label the anterior portion of cerebral arteries using a statistical inference method on the Bayesian network representation of the vessel tree. Our approach combines the likelihoods obtained from a random forest classifier trained using vessel centerline features with a belief propagation method integrating the connection probabilities of the cerebral artery network. We evaluate our method on 30 subjects using a leave-one-out validation, and show that it achieves an average correct vessel labeling rate of over 92%.

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866918 (13 March 2013); doi: 10.1117/12.2006460
Show Author Affiliations
Murat Bilgel, Johns Hopkins Univ. School of Medicine (United States)
Snehashis Roy, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Paul A. Nyquist, Johns Hopkins Univ. School of Medicine (United States)
Jerry L. Prince, Johns Hopkins Univ. School of Medicine (United States)
Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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