Cortical reconstruction using implicit surface evolution: Accuracy and precision analysis

Duygu Tosun, Maryam E. Rettmann, Daniel Q. Naiman, Susan M. Resnick, Michael A. Kraut, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)838-852
Number of pages15
JournalNeuroImage
Volume29
Issue number3
DOIs
StatePublished - Feb 1 2006

Keywords

  • ANOVA
  • Accuracy analysis
  • Cerebral cortex
  • Cortical reconstruction
  • Human brain mapping
  • MANOVA
  • Magnetic resonance
  • Precision analysis
  • T1-weighted MR brain images

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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