Automated anatomical labeling of the cerebral arteries using belief propagation

Murat Bilgel, Snehashis Roy, Aaron Carass, Paul A. Nyquist, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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%.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2013
Subtitle of host publicationImage Processing
DOIs
StatePublished - Jun 3 2013
EventMedical Imaging 2013: Image Processing - Lake Buena Vista, FL, United States
Duration: Feb 10 2013Feb 12 2013

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8669
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2013: Image Processing
CountryUnited States
CityLake Buena Vista, FL
Period2/10/132/12/13

Keywords

  • Automated labeling of vessels
  • Belief propagation
  • Cerebral arteries
  • Random forest
  • Statistical inference on bayesian networks

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Bilgel, M., Roy, S., Carass, A., Nyquist, P. A., & Prince, J. L. (2013). Automated anatomical labeling of the cerebral arteries using belief propagation. In Medical Imaging 2013: Image Processing [866918] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8669). https://doi.org/10.1117/12.2006460