Automated parcellation of the brain surface generated from magnetic resonance images

Wen Li; Nancy C Andreasen; Peg Nopoulos; Vincent A Magnotta

doi:10.3389/fninf.2013.00023

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Automated parcellation of the brain surface generated from magnetic resonance images

Journal article

Open access

Peer reviewed

Automated parcellation of the brain surface generated from magnetic resonance images

Wen Li, Nancy C Andreasen, Peg Nopoulos and Vincent A Magnotta

Frontiers in neuroinformatics, Vol.7, pp.23-23

10/22/2013

DOI: 10.3389/fninf.2013.00023

PMCID: PMC3804771

PMID: 24155718

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https://doi.org/10.3389/fninf.2013.00023View

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Abstract

We have developed a fast and reliable pipeline to automatically parcellate the cortical surface into sub-regions. The pipeline can be used to study brain changes associated with psychiatric and neurological disorders. First, a genus zero cortical surface for one hemisphere is generated from the magnetic resonance images at the parametric boundary of the white matter and the gray matter. Second, a hemisphere-specific surface atlas is registered to the cortical surface using geometry features mapped in the spherical domain. The deformation field is used to warp statistic labels from the atlas to the subject surface. The Dice index of the labeled surface area is used to evaluate the similarity between the automated labels with the manual labels on the subject. The average Dice across 24 regions on 14 testing subjects is 0.86. Alternative evaluations have also chosen to show the accuracy and flexibility of the present method. The point-wise accuracy of 14 testing subjects is above 86% in average. The experiment shows that the present method is highly consistent with FreeSurfer (>99% of the surface area), using the same set of labels.

Cerebral Cortex

Neuroscience

Magnetic Resonance Imaging

spherical demons

surface registration

cortical parcellation

cytoarchitecture

Details

Title: Subtitle: Automated parcellation of the brain surface generated from magnetic resonance images
Creators: Wen Li - Department of Biomedical Engineering, The University of Iowa
Nancy C Andreasen - Department of Psychiatry, The University of Iowa Roy and Lucille Carver College of Medicine
Peg Nopoulos - Department of Psychiatry, The University of Iowa Roy and Lucille Carver College of Medicine
Vincent A Magnotta - Department of Biomedical Engineering, The University of Iowa
Resource Type: Journal article
Publication Details: Frontiers in neuroinformatics, Vol.7, pp.23-23
DOI: 10.3389/fninf.2013.00023
PMID: 24155718
PMCID: PMC3804771
NLM abbreviation: Front Neuroinform
ISSN: 1662-5196
eISSN: 1662-5196
Publisher: Frontiers Media S.A
Language: English
Date published: 10/22/2013
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Neurology; Radiology; Psychiatry; Stead Family Department of Pediatrics; Iowa Neuroscience Institute
Record Identifier: 9984051582002771

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