Automatic segmentation of neonatal images using convex optimization and coupled level set method

Li Wang; Feng Shi; John H. Gilmore; Weili Lin; Dinggang Shen

doi:10.1007/978-3-642-15699-1_1

Automatic segmentation of neonatal images using convex optimization and coupled level set method

Li Wang, Feng Shi, John H. Gilmore, Weili Lin, Dinggang Shen

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

2 Citations (Scopus)

Abstract

Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.

Original language	English
Title of host publication	Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings
Pages	1-10
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-15699-1_1
Publication status	Published - 2010
Externally published	Yes
Event	5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010 - Beijing, China Duration: 2010 Sep 19 → 2010 Sep 20

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

Other

Other	5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010
Country	China
City	Beijing
Period	10/9/19 → 10/9/20

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-15699-1_1

Fingerprint Dive into the research topics of 'Automatic segmentation of neonatal images using convex optimization and coupled level set method'. Together they form a unique fingerprint.

Cite this

Wang, L., Shi, F., Gilmore, J. H., Lin, W., & Shen, D. (2010). Automatic segmentation of neonatal images using convex optimization and coupled level set method. In Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings (pp. 1-10). (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_1

Automatic segmentation of neonatal images using convex optimization and coupled level set method. / Wang, Li; Shi, Feng; Gilmore, John H.; Lin, Weili; Shen, Dinggang.

Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. 2010. p. 1-10 (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

Wang, L, Shi, F, Gilmore, JH, Lin, W & Shen, D 2010, Automatic segmentation of neonatal images using convex optimization and coupled level set method. 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. 1-10, 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010, Beijing, China, 10/9/19. https://doi.org/10.1007/978-3-642-15699-1_1

Wang L, Shi F, Gilmore JH, Lin W, Shen D. Automatic segmentation of neonatal images using convex optimization and coupled level set method. In Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. 2010. p. 1-10. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-15699-1_1

Wang, Li ; Shi, Feng ; Gilmore, John H. ; Lin, Weili ; Shen, Dinggang. / Automatic segmentation of neonatal images using convex optimization and coupled level set method. Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. 2010. pp. 1-10 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a30ac36066b24e6ebc3097b335219b28,

title = "Automatic segmentation of neonatal images using convex optimization and coupled level set method",

abstract = "Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.",

author = "Li Wang and Feng Shi and Gilmore, {John H.} and Weili Lin and Dinggang Shen",

year = "2010",

doi = "10.1007/978-3-642-15699-1_1",

language = "English",

isbn = "3642156983",

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

pages = "1--10",

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",

}

TY - GEN

T1 - Automatic segmentation of neonatal images using convex optimization and coupled level set method

AU - Wang, Li

AU - Shi, Feng

AU - Gilmore, John H.

AU - Lin, Weili

AU - Shen, Dinggang

PY - 2010

Y1 - 2010

N2 - Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.

AB - Accurate segmentation of neonatal brain MR images remains challenging mainly due to poor spatial resolution, low tissue contrast, high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although parametric or geometric deformable models have been successfully applied to adult brain segmentation, to the best of our knowledge, they are not explored in neonatal images. In this paper, we propose a novel neonatal image segmentation method, combining local intensity information, atlas spatial prior and cortical thickness constraint, in a level set framework. Besides, we also provide a robust and reliable tissue surfaces initialization for our proposed level set method by using a convex optimization technique. Validation is performed on 10 neonatal brain images with promising results.

UR - http://www.scopus.com/inward/record.url?scp=78049449817&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78049449817&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-15699-1_1

DO - 10.1007/978-3-642-15699-1_1

M3 - Conference contribution

AN - SCOPUS:78049449817

SN - 3642156983

SN - 9783642156984

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

SP - 1

EP - 10

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 -