title: Cortical reconstruction using implicit surface evolution: accuracy and precision analysis.
abstract: Two different studies were conducted to assess the accuracy and precision of an algorithm developed for automatic reconstruction of the cerebral cortex from T1-weighted magnetic resonance (MR) brain images. Repeated scans of three different brains were used to quantify the precision of the algorithm, and manually selected landmarks on different sulcal regions throughout the cortex were used to analyze the accuracy of the three reconstructed surfaces: inner, central, and pial. We conclude that the algorithm can find these surfaces in a robust fashion and with subvoxel accuracy, typically with an accuracy of one third of a voxel, although this varies with brain region and cortical geometry. Parameters were adjusted on the basis of this analysis in order to improve the algorithm's overall performance.
authors.affiliation: Department of Electrical and Computer Engineering, The Johns Hopkins University, 3400 North Charles Street, 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: Daniel Q
authors.initials: DQ
authors.lastname: Naiman
authors.forename: Susan M
authors.initials: SM
authors.lastname: Resnick
authors.forename: Michael A
authors.initials: MA
authors.lastname: Kraut
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000502:physiology; D003627:Data Interpretation, Statistical; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010841:Pia Mater / Q000033:anatomy & histology; D015203:Reproducibility of Results