Automated anatomical labeling of the cerebral arteries using belief propagation
Murat Bilgel, Snehashis Roy, Aaron Carass, Paul Nyquist, Jerry L. Prince · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
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%.