Cited by 13,324Medical Image Segmentation TechniquesFunctional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

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An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

Desikan, Rahul S., Ségonne, Florent, Fischl, Bruce, Quinn, Brian T., Dickerson, Bradford C., Blacker, Deborah, Buckner, Randy L., Dale, Anders M., Maguire, R. Paul, Hyman, Bradley T., Albert, Marilyn S., Killiany, Ronald J.·2006·NeuroImage

DOI 10.1016/j.neuroimage.2006.01.021

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Abstract

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment…

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Authors: Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J.
Affiliations: Boston University (US)
MIT Computer Science and Artificial Intelligence Laboratory (US)
Massachusetts General Hospital (US)
Howard Hughes Medical Institute (US)
University of California San Diego (US)
Pfizer (United States) (US)
Johns Hopkins University (US)
Corresponding author: Killiany, Ronald J.
ORCID: Desikan, Rahul S.
Fischl, Bruce
Dickerson, Bradford C.
Blacker, Deborah
Buckner, Randy L.
Dale, Anders M.
Maguire, R. Paul
Hyman, Bradley T.
Albert, Marilyn S.
Killiany, Ronald J.
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2006-07
Volume: 31
Issue: 3
Pages: 968-980
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2006.01.021
OpenAlex: W2101135654
Cited by: 13,324
References: 66
Citations by year: 2026: 1062 · 2025: 1406 · 2024: 1305 · 2023: 1215 · 2022: 1329 · 2021: 1207 · 2020: 1104 · 2019: 886 · 2018: 764 · 2017: 641 · 2016: 548 · 2015: 510 · 2014: 355 · 2013: 333 · 2012: 198
FWCI: 61.04
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
Funders: National Institute on Aging (P01-AG04953)
National Center for Research Resources (R01 RR 16594-01A1, P41-RR14075, BIRN002, U24 RR021382)
National Institute of Biomedical Imaging and Bioengineering (R01 EB001550)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Keywords: human cerebral cortex, cortical parcellation, brain atlas, inter-rater reliability, intraclass correlation coefficient, morphometrics, treatment response monitoring
MeSH terms: Adult, Aged, Aged, 80 and over, Aging, Algorithms, Alzheimer Disease, Atrophy, Brain Mapping, Cerebral Cortex, Corpus Callosum, Dominance, Cerebral, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Software, Statistics as Topic, Reproducibility of Results, Observer Variation, Imaging, Three-Dimensional

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An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

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