Automatic cortical gyral parcellation using probabilistic atlas and graph cuts

Gang Li; Lei Guo; Tuo Zhang; Jingxin Nie; Tianming Liu

doi:10.1007/978-3-642-15699-1_46

Automatic cortical gyral parcellation using probabilistic atlas and graph cuts

Gang Li
, Lei Guo
, Tuo Zhang
, Jingxin Nie
, Tianming Liu

School of Automation

Northwestern Polytechnical University Xian
University of Georgia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Automatic parcellation of cortical surfaces into sulci or gyri based regions is of great importance in studying the structure and function of the human brain. This paper presents a novel method for automatic parcellation of cortical surfaces into gyri based regions. The method is composed of two major steps: data-driven gyral patch segmentation and model-driven gyral patch labeling. The gyral patch segmentation is achieved by several steps, including sulcal region segmentation, sulcal basin parcellation, gyral crest segments extraction and gyral patch segmentation. The gyral patch labeling is formulated as an energy minimization problem, in which a cortical probabilistic atlas and the curvature information on surfaces are used to define the energy function. The energy function is efficiently solved by the graph cuts method. A unique feature of the proposed method is that it does not require high dimensional spatial normalization on images or surfaces. The method has been successfully applied to cortical surfaces of 15 young healthy brain MR images. Quantitative and qualitative evaluation results demonstrate the validity of the proposed method.

Original language	English
Title of host publication	Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings
Pages	438-447
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-15699-1_46
State	Published - 2010
Event	5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010 - Beijing, China Duration: 19 Sep 2010 → 20 Sep 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6326 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010
Country/Territory	China
City	Beijing
Period	19/09/10 → 20/09/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

graph cuts
gyral basin
gyral parcellation
probabilistic atlas

Access to Document

10.1007/978-3-642-15699-1_46

Cite this

Li, G., Guo, L., Zhang, T., Nie, J., & Liu, T. (2010). Automatic cortical gyral parcellation using probabilistic atlas and graph cuts. In Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings (pp. 438-447). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6326 LNCS). https://doi.org/10.1007/978-3-642-15699-1_46

@inproceedings{e82b4c2d39324649bc764dfefcec1bd6,

title = "Automatic cortical gyral parcellation using probabilistic atlas and graph cuts",

abstract = "Automatic parcellation of cortical surfaces into sulci or gyri based regions is of great importance in studying the structure and function of the human brain. This paper presents a novel method for automatic parcellation of cortical surfaces into gyri based regions. The method is composed of two major steps: data-driven gyral patch segmentation and model-driven gyral patch labeling. The gyral patch segmentation is achieved by several steps, including sulcal region segmentation, sulcal basin parcellation, gyral crest segments extraction and gyral patch segmentation. The gyral patch labeling is formulated as an energy minimization problem, in which a cortical probabilistic atlas and the curvature information on surfaces are used to define the energy function. The energy function is efficiently solved by the graph cuts method. A unique feature of the proposed method is that it does not require high dimensional spatial normalization on images or surfaces. The method has been successfully applied to cortical surfaces of 15 young healthy brain MR images. Quantitative and qualitative evaluation results demonstrate the validity of the proposed method.",

keywords = "graph cuts, gyral basin, gyral parcellation, probabilistic atlas",

author = "Gang Li and Lei Guo and Tuo Zhang and Jingxin Nie and Tianming Liu",

year = "2010",

doi = "10.1007/978-3-642-15699-1\_46",

language = "英语",

isbn = "3642156983",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "438--447",

booktitle = "Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings",

note = "5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010 ; Conference date: 19-09-2010 Through 20-09-2010",

}

Li, G, Guo, L , Zhang, T, Nie, J & Liu, T 2010, Automatic cortical gyral parcellation using probabilistic atlas and graph cuts. in Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6326 LNCS, pp. 438-447, 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010, Beijing, China, 19/09/10. https://doi.org/10.1007/978-3-642-15699-1_46

Automatic cortical gyral parcellation using probabilistic atlas and graph cuts. / Li, Gang; Guo, Lei ; Zhang, Tuo et al.
Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. 2010. p. 438-447 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6326 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Automatic cortical gyral parcellation using probabilistic atlas and graph cuts

AU - Li, Gang

AU - Guo, Lei

AU - Zhang, Tuo

AU - Nie, Jingxin

AU - Liu, Tianming

PY - 2010

Y1 - 2010

N2 - Automatic parcellation of cortical surfaces into sulci or gyri based regions is of great importance in studying the structure and function of the human brain. This paper presents a novel method for automatic parcellation of cortical surfaces into gyri based regions. The method is composed of two major steps: data-driven gyral patch segmentation and model-driven gyral patch labeling. The gyral patch segmentation is achieved by several steps, including sulcal region segmentation, sulcal basin parcellation, gyral crest segments extraction and gyral patch segmentation. The gyral patch labeling is formulated as an energy minimization problem, in which a cortical probabilistic atlas and the curvature information on surfaces are used to define the energy function. The energy function is efficiently solved by the graph cuts method. A unique feature of the proposed method is that it does not require high dimensional spatial normalization on images or surfaces. The method has been successfully applied to cortical surfaces of 15 young healthy brain MR images. Quantitative and qualitative evaluation results demonstrate the validity of the proposed method.

AB - Automatic parcellation of cortical surfaces into sulci or gyri based regions is of great importance in studying the structure and function of the human brain. This paper presents a novel method for automatic parcellation of cortical surfaces into gyri based regions. The method is composed of two major steps: data-driven gyral patch segmentation and model-driven gyral patch labeling. The gyral patch segmentation is achieved by several steps, including sulcal region segmentation, sulcal basin parcellation, gyral crest segments extraction and gyral patch segmentation. The gyral patch labeling is formulated as an energy minimization problem, in which a cortical probabilistic atlas and the curvature information on surfaces are used to define the energy function. The energy function is efficiently solved by the graph cuts method. A unique feature of the proposed method is that it does not require high dimensional spatial normalization on images or surfaces. The method has been successfully applied to cortical surfaces of 15 young healthy brain MR images. Quantitative and qualitative evaluation results demonstrate the validity of the proposed method.

KW - graph cuts

KW - gyral basin

KW - gyral parcellation

KW - probabilistic atlas

UR - https://www.scopus.com/pages/publications/78049435273

U2 - 10.1007/978-3-642-15699-1_46

DO - 10.1007/978-3-642-15699-1_46

M3 - 会议稿件

AN - SCOPUS:78049435273

SN - 3642156983

SN - 9783642156984

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

SP - 438

EP - 447

BT - Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings

T2 - 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010

Y2 - 19 September 2010 through 20 September 2010

ER -

Li G, Guo L , Zhang T, Nie J, Liu T. Automatic cortical gyral parcellation using probabilistic atlas and graph cuts. In Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. 2010. p. 438-447. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-15699-1_46

Automatic cortical gyral parcellation using probabilistic atlas and graph cuts

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this