Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

Xiao Han; Chenyang Xu; Ulisses Braga-Neto; Jerry L. Prince

doi:10.1109/42.993130

Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

Xiao Han, Chenyang Xu, Ulisses Braga-Neto, Jerry L. Prince

Whiting School of Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy recently reported a fully automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.

Original language	English (US)
Pages (from-to)	109-121
Number of pages	13
Journal	IEEE transactions on medical imaging
Volume	21
Issue number	2
DOIs	https://doi.org/10.1109/42.993130
State	Published - Feb 2002

Keywords

Brain imaging
Cortical surface extraction
Digital topology
Graph analysis
Segmentation

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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title = "Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm",

abstract = "Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy recently reported a fully automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.",

keywords = "Brain imaging, Cortical surface extraction, Digital topology, Graph analysis, Segmentation",

author = "Xiao Han and Chenyang Xu and Ulisses Braga-Neto and Prince, {Jerry L.}",

note = "Funding Information: Manuscript received June 7, 2001; revised January 8, 2002. This work was supported in part by the National Science Foundation(NSF)/Engineering Research Center (ERC) under Grant CISST 9731748 and in part by the National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS) under Grant R01NS37747.The Associate Editor responsible for co-ordinating the review of this paper and recommending its publication was R. Leahy. Asterisk indicates corresponding author. Copyright: Copyright 2008 Elsevier B.V., All rights reserved.",

year = "2002",

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doi = "10.1109/42.993130",

language = "English (US)",

volume = "21",

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AU - Han, Xiao

AU - Xu, Chenyang

AU - Braga-Neto, Ulisses

AU - Prince, Jerry L.

N1 - Funding Information: Manuscript received June 7, 2001; revised January 8, 2002. This work was supported in part by the National Science Foundation(NSF)/Engineering Research Center (ERC) under Grant CISST 9731748 and in part by the National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS) under Grant R01NS37747.The Associate Editor responsible for co-ordinating the review of this paper and recommending its publication was R. Leahy. Asterisk indicates corresponding author. Copyright: Copyright 2008 Elsevier B.V., All rights reserved.

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N2 - Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy recently reported a fully automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.

AB - Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy recently reported a fully automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.

KW - Brain imaging

KW - Cortical surface extraction

KW - Digital topology

KW - Graph analysis

KW - Segmentation

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Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

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Keywords

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