Automatic cardiac MRI segmentation using a biventricular deformable medial model

Hui Sun; Alejandro F. Frangi; Hongzhi Wang; Federico M. Sukno; Catalina Tobon-Gomez; Paul A. Yushkevich

doi:10.1007/978-3-642-15705-9_57

Automatic cardiac MRI segmentation using a biventricular deformable medial model

Hui Sun^*, Alejandro F. Frangi, Hongzhi Wang, Federico M. Sukno, Catalina Tobon-Gomez, Paul A. Yushkevich

^*Corresponding author for this work

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

Abstract

We present a novel approach for automatic segmentation of the myocardium in short-axis MRI using deformable medial models with an explicit representation of thickness. Segmentation is constrained by a Markov prior on myocardial thickness. Best practices from Active Shape Modeling (global PCA shape prior, statistical appearance model, local search) are adapted to the medial model. Segmentation performance is evaluated by comparing to manual segmentation in a heterogeneous adult MRI dataset. Average boundary displacement error is under 1.4 mm for left and right ventricles, comparing favorably with published work.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Pages	468-475
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-15705-9_57
Publication status	Published - 2010
Event	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China Duration: 20 Sept 2010 → 24 Sept 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6361 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Country/Territory	China
City	Beijing
Period	20/09/10 → 24/09/10

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10.1007/978-3-642-15705-9_57

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Sun, H., Frangi, A. F., Wang, H., Sukno, F. M., Tobon-Gomez, C., & Yushkevich, P. A. (2010). Automatic cardiac MRI segmentation using a biventricular deformable medial model. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings (PART 1 ed., pp. 468-475). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6361 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-15705-9_57

Sun, Hui ; Frangi, Alejandro F. ; Wang, Hongzhi et al. / Automatic cardiac MRI segmentation using a biventricular deformable medial model. Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 1. ed. 2010. pp. 468-475 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Automatic cardiac MRI segmentation using a biventricular deformable medial model",

abstract = "We present a novel approach for automatic segmentation of the myocardium in short-axis MRI using deformable medial models with an explicit representation of thickness. Segmentation is constrained by a Markov prior on myocardial thickness. Best practices from Active Shape Modeling (global PCA shape prior, statistical appearance model, local search) are adapted to the medial model. Segmentation performance is evaluated by comparing to manual segmentation in a heterogeneous adult MRI dataset. Average boundary displacement error is under 1.4 mm for left and right ventricles, comparing favorably with published work.",

author = "Hui Sun and Frangi, {Alejandro F.} and Hongzhi Wang and Sukno, {Federico M.} and Catalina Tobon-Gomez and Yushkevich, {Paul A.}",

year = "2010",

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

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booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings",

edition = "PART 1",

note = "13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 ; Conference date: 20-09-2010 Through 24-09-2010",

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Sun, H, Frangi, AF, Wang, H, Sukno, FM, Tobon-Gomez, C & Yushkevich, PA 2010, Automatic cardiac MRI segmentation using a biventricular deformable medial model. in Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6361 LNCS, pp. 468-475, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010, Beijing, China, 20/09/10. https://doi.org/10.1007/978-3-642-15705-9_57

Automatic cardiac MRI segmentation using a biventricular deformable medial model. / Sun, Hui; Frangi, Alejandro F.; Wang, Hongzhi et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 1. ed. 2010. p. 468-475 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6361 LNCS, No. PART 1).

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

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AU - Tobon-Gomez, Catalina

AU - Yushkevich, Paul A.

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AB - We present a novel approach for automatic segmentation of the myocardium in short-axis MRI using deformable medial models with an explicit representation of thickness. Segmentation is constrained by a Markov prior on myocardial thickness. Best practices from Active Shape Modeling (global PCA shape prior, statistical appearance model, local search) are adapted to the medial model. Segmentation performance is evaluated by comparing to manual segmentation in a heterogeneous adult MRI dataset. Average boundary displacement error is under 1.4 mm for left and right ventricles, comparing favorably with published work.

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Sun H, Frangi AF, Wang H, Sukno FM, Tobon-Gomez C, Yushkevich PA. Automatic cardiac MRI segmentation using a biventricular deformable medial model. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings. PART 1 ed. 2010. p. 468-475. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-15705-9_57

Automatic cardiac MRI segmentation using a biventricular deformable medial model

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