title: Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain.
abstract: A method for automated segmentation of major cortical sulci on the outer brain boundary is presented, with emphasis on automatically determining point correspondence and on labeling cortical regions. The method is formulated in a general optimization framework defined on the unit sphere, which serves as parametric domain for convoluted surfaces of spherical topology. A statistical shape model, which includes a network of deformable curves on the unit sphere, seeks geometric features such as high curvature regions and labels such features via a deformation process that is confined within a spherical map of the outer brain boundary. The limitations of the customary spherical coordinate system, which include discontinuities at the poles and nonuniform sampling, are overcome by defining the statistical prior of shape variation in terms of projections of landmark points onto corresponding tangent planes of the sphere. The method is tested against and shown to be as accurate as manually defined segmentations.
authors.affiliation: Department of Elecltrical and Computer Engineering and the Center for Biomedical Image Computing, School of Medicine, Johns Hopkins University, Baltimore, MD 21218 USA.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Tao
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
mesh_terms: D000465:Algorithms; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D016208:Databases, Factual; D006801:Humans; D007089:Image Enhancement; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity