AUTOMATED ANATOMICAL LABELING OF THE CEREBRAL ARTERIES USING BELIEF PROPAGATION.
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%.
Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Murat
M
Bilgel
Snehashis
S
Roy
Aaron
A
Carass
Paul A
PA
Nyquist
Jerry L
JL
Prince
Automated labeling of vessels; belief propagation; cerebral arteries; random forest; statistical inference on Bayesian networks