title: Automated sulci identification via intrinsic modeling of cortical anatomy.
abstract: In this paper we propose a novel and robust system for the automated identification of major sulci on cortical surfaces. Using multiscale representation and intrinsic surface mapping, our system encodes anatomical priors in manually traced sulcal lines with an intrinsic atlas of major sulci. This allows the computation of both individual and joint likelihood of sulcal lines for their automatic identification on cortical surfaces. By modeling sulcal anatomy with intrinsic geometry, our system is invariant to pose differences and robust across populations and surface extraction methods. In our experiments, we present quantitative validations on twelve major sulci to show the excellent agreement of our results with manually traced curves. We also demonstrate the robustness of our system by successfully applying an atlas of Chinese population to identify sulci on Caucasian brains of different age groups, and surfaces extracted by three popular software tools.
authors.affiliation: Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA. yshi@loni.ucla.edu
authors.forename: Yonggang
authors.initials: Y
authors.lastname: Shi
authors.forename: Bo
authors.initials: B
authors.lastname: Sun
authors.forename: Rongjie
authors.initials: R
authors.lastname: Lai
authors.forename: Ivo
authors.initials: I
authors.lastname: Dinov
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
mesh_terms: D000465:Algorithms; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity