Measuring the thickness of the human cerebral cortex from magnetic resonance images
Bruce Fischl,Anders M. Dale +1 more
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TLDR
An automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy is presented.Abstract:
Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.read more
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An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
Rahul S. Desikan,Florent Ségonne,Bruce Fischl,Bruce Fischl,Brian T. Quinn,Bradford C. Dickerson,Deborah Blacker,Randy L. Buckner,Randy L. Buckner,Anders M. Dale,R. Paul Maguire,Bradley T. Hyman,Marilyn S. Albert,Ronald J. Killiany +13 more
TL;DR: An automated labeling system for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable and may be useful for both morphometric and functional studies of the cerebral cortex.
Journal ArticleDOI
Automatically Parcellating the Human Cerebral Cortex
Bruce Fischl,Andre van der Kouwe,Christophe Destrieux,Eric Halgren,Florent Ségonne,David H. Salat,Evelina Busa,Larry J. Seidman,Jill M. Goldstein,David N. Kennedy,Verne S. Caviness,Nikos Makris,Bruce R. Rosen,Anders M. Dale +13 more
TL;DR: A technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from a manually labeled training set is presented, comparable in accuracy to manual labeling.
Journal ArticleDOI
A reproducible evaluation of ANTs similarity metric performance in brain image registration.
TL;DR: This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling, and to quantify the similarity of templates derived from different subgroups.
Journal ArticleDOI
Accurate and robust brain image alignment using boundary-based registration.
Douglas N. Greve,Bruce Fischl +1 more
TL;DR: Visual inspection and fMRI results show that BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities.
Journal ArticleDOI
Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis
TL;DR: This paper introduces a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points and successfully reduces variability and avoids over-regularization.
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Level set methods and fast marching methods : evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science
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