title: Cortical surface segmentation and mapping.
abstract: Segmentation and mapping of the human cerebral cortex from magnetic resonance (MR) images plays an important role in neuroscience and medicine. This paper describes a comprehensive approach for cortical reconstruction, flattening, and sulcal segmentation. Robustness to imaging artifacts and anatomical consistency are key achievements in an overall approach that is nearly fully automatic and computationally fast. Results demonstrating the application of this approach to a study of cortical thickness changes in aging are presented.
authors.affiliation: Department of Electrical and Computer, Engineering Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: Duygu
authors.initials: D
authors.lastname: Tosun
authors.forename: Maryam E
authors.initials: ME
authors.lastname: Rettmann
authors.forename: Xiao
authors.initials: X
authors.lastname: Han
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Tao
authors.forename: Chenyang
authors.initials: C
authors.lastname: Xu
authors.forename: Susan M
authors.initials: SM
authors.lastname: Resnick
authors.forename: Dzung L
authors.initials: DL
authors.lastname: Pham
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D000375:Aging / Q000502:physiology; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000254:growth & development / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D009417:Nerve Tissue / Q000033:anatomy & histology; D015203:Reproducibility of Results