Partition cortical surfaces into supervertices: Method and application

Gang Li, Jingxin Nie, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Many problems in computer vision and biomedical image analysis benefit from representing an image as a set of superpixels or supervoxels. Inspired by this, we propose to partition a cortical surface into a collection of small patches, namely supervertices, with quasi-uniform size and coverage, compactness, and also smooth boundaries that align sulcal fundi or gyral crest curves on cortical surfaces. The ultimate goal of supervertices partition of the cortical surfaces is to use supervertices as primitives for cortical surface analysis, such as the extraction of sulcal fundi or gyral crest curves by linking boundaries of supervertices, and also the parcellation of cortical surfaces by labeling supervertices instead of vertices. We formulate the supervertices partition as an energy minimization problem and optimize it with graph cuts. Specifically, our energy function encourages the supervertices with compact shapes and smooth boundaries at flat cortical regions and also the supervertices with boundaries aligned with the sulcal fundi or gyral crest curves at highly bended cortical regions. The method has been successfully applied to cortical surfaces of brain MR images in NAMIC and MSC datasets. Both qualitative and quantitative evaluation results demonstrate its validity. We also show an application, i.e., extraction of gyral crest curves on cortical surfaces by linking boundaries of supervertices.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages112-121
Number of pages10
Volume7599 LNCS
DOIs
Publication statusPublished - 2012 Dec 27
Externally publishedYes
EventMICCAI 2012 International Workshop on Mesh Processing in Medical Image Analysis, MeshMed 2012 - Nice, France
Duration: 2012 Oct 12012 Oct 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7599 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherMICCAI 2012 International Workshop on Mesh Processing in Medical Image Analysis, MeshMed 2012
CountryFrance
CityNice
Period12/10/112/10/1

Fingerprint

Partition
Curve
Linking
Surface analysis
Graph Cuts
Energy Minimization
Quantitative Evaluation
Labeling
Image analysis
Computer vision
Energy Function
Brain
Image Analysis
Computer Vision
Minimization Problem
Patch
Compactness
Coverage
Optimise
Demonstrate

Keywords

  • cortical surface partition
  • graph cuts
  • gyral crest curves
  • sulcal fundi
  • Superpixels
  • supervertices

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Li, G., Nie, J., & Shen, D. (2012). Partition cortical surfaces into supervertices: Method and application. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7599 LNCS, pp. 112-121). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7599 LNCS). https://doi.org/10.1007/978-3-642-33463-4_12

Partition cortical surfaces into supervertices : Method and application. / Li, Gang; Nie, Jingxin; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7599 LNCS 2012. p. 112-121 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7599 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, G, Nie, J & Shen, D 2012, Partition cortical surfaces into supervertices: Method and application. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7599 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7599 LNCS, pp. 112-121, MICCAI 2012 International Workshop on Mesh Processing in Medical Image Analysis, MeshMed 2012, Nice, France, 12/10/1. https://doi.org/10.1007/978-3-642-33463-4_12
Li G, Nie J, Shen D. Partition cortical surfaces into supervertices: Method and application. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7599 LNCS. 2012. p. 112-121. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-33463-4_12
Li, Gang ; Nie, Jingxin ; Shen, Dinggang. / Partition cortical surfaces into supervertices : Method and application. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7599 LNCS 2012. pp. 112-121 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{b96cbf9d0c1043dda10aa34c77738ab1,
title = "Partition cortical surfaces into supervertices: Method and application",
abstract = "Many problems in computer vision and biomedical image analysis benefit from representing an image as a set of superpixels or supervoxels. Inspired by this, we propose to partition a cortical surface into a collection of small patches, namely supervertices, with quasi-uniform size and coverage, compactness, and also smooth boundaries that align sulcal fundi or gyral crest curves on cortical surfaces. The ultimate goal of supervertices partition of the cortical surfaces is to use supervertices as primitives for cortical surface analysis, such as the extraction of sulcal fundi or gyral crest curves by linking boundaries of supervertices, and also the parcellation of cortical surfaces by labeling supervertices instead of vertices. We formulate the supervertices partition as an energy minimization problem and optimize it with graph cuts. Specifically, our energy function encourages the supervertices with compact shapes and smooth boundaries at flat cortical regions and also the supervertices with boundaries aligned with the sulcal fundi or gyral crest curves at highly bended cortical regions. The method has been successfully applied to cortical surfaces of brain MR images in NAMIC and MSC datasets. Both qualitative and quantitative evaluation results demonstrate its validity. We also show an application, i.e., extraction of gyral crest curves on cortical surfaces by linking boundaries of supervertices.",
keywords = "cortical surface partition, graph cuts, gyral crest curves, sulcal fundi, Superpixels, supervertices",
author = "Gang Li and Jingxin Nie and Dinggang Shen",
year = "2012",
month = "12",
day = "27",
doi = "10.1007/978-3-642-33463-4_12",
language = "English",
isbn = "9783642334627",
volume = "7599 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "112--121",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Partition cortical surfaces into supervertices

T2 - Method and application

AU - Li, Gang

AU - Nie, Jingxin

AU - Shen, Dinggang

PY - 2012/12/27

Y1 - 2012/12/27

N2 - Many problems in computer vision and biomedical image analysis benefit from representing an image as a set of superpixels or supervoxels. Inspired by this, we propose to partition a cortical surface into a collection of small patches, namely supervertices, with quasi-uniform size and coverage, compactness, and also smooth boundaries that align sulcal fundi or gyral crest curves on cortical surfaces. The ultimate goal of supervertices partition of the cortical surfaces is to use supervertices as primitives for cortical surface analysis, such as the extraction of sulcal fundi or gyral crest curves by linking boundaries of supervertices, and also the parcellation of cortical surfaces by labeling supervertices instead of vertices. We formulate the supervertices partition as an energy minimization problem and optimize it with graph cuts. Specifically, our energy function encourages the supervertices with compact shapes and smooth boundaries at flat cortical regions and also the supervertices with boundaries aligned with the sulcal fundi or gyral crest curves at highly bended cortical regions. The method has been successfully applied to cortical surfaces of brain MR images in NAMIC and MSC datasets. Both qualitative and quantitative evaluation results demonstrate its validity. We also show an application, i.e., extraction of gyral crest curves on cortical surfaces by linking boundaries of supervertices.

AB - Many problems in computer vision and biomedical image analysis benefit from representing an image as a set of superpixels or supervoxels. Inspired by this, we propose to partition a cortical surface into a collection of small patches, namely supervertices, with quasi-uniform size and coverage, compactness, and also smooth boundaries that align sulcal fundi or gyral crest curves on cortical surfaces. The ultimate goal of supervertices partition of the cortical surfaces is to use supervertices as primitives for cortical surface analysis, such as the extraction of sulcal fundi or gyral crest curves by linking boundaries of supervertices, and also the parcellation of cortical surfaces by labeling supervertices instead of vertices. We formulate the supervertices partition as an energy minimization problem and optimize it with graph cuts. Specifically, our energy function encourages the supervertices with compact shapes and smooth boundaries at flat cortical regions and also the supervertices with boundaries aligned with the sulcal fundi or gyral crest curves at highly bended cortical regions. The method has been successfully applied to cortical surfaces of brain MR images in NAMIC and MSC datasets. Both qualitative and quantitative evaluation results demonstrate its validity. We also show an application, i.e., extraction of gyral crest curves on cortical surfaces by linking boundaries of supervertices.

KW - cortical surface partition

KW - graph cuts

KW - gyral crest curves

KW - sulcal fundi

KW - Superpixels

KW - supervertices

UR - http://www.scopus.com/inward/record.url?scp=84871446513&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84871446513&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-33463-4_12

DO - 10.1007/978-3-642-33463-4_12

M3 - Conference contribution

AN - SCOPUS:84871446513

SN - 9783642334627

VL - 7599 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 112

EP - 121

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -