Segmentation of Left Ventricle with a Coupled Length Regularization and Sparse Composite Shape Prior: a Variational Approach

Wenyang Liu; Dan Ruan; Wenyang Liu; Dan Ruan

doi:10.3934/medsci.2015.4.295

AIMS Medical Science

2015, Volume 2, Issue 4: 295-302. doi: 10.3934/medsci.2015.4.295

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Research article Special Issues

Segmentation of Left Ventricle with a Coupled Length Regularization and Sparse Composite Shape Prior: a Variational Approach

Wenyang Liu ¹,
Dan Ruan ^{1,2
,
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1.
Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
2.
Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA

Received: 25 May 2015 Accepted: 05 September 2015 Published: 14 September 2015

Segmentation of left ventricles in Cine MR images plays an important role in analyzing cardiac functions. In this study, we propose a variational method that incorporates both prior knowledge on geometrical coupling and shapes of the endo- and epicardium. Specifically, we dynamically maintain and update a smoothly varying distance between the endo- and epicardial contours, represented by a pair of level set functions, with a novel coupling energy embedded in the length regularization. We encode the shape prior with a sparse composite model based on a set of training templates. A robust fidelity with Gaussian mixture models is employed to provide robust intensity estimates in each subregion under insufficient local gradient information. Quantitative evaluation of the proposed method demonstrates competitive/better DSC and APD accuracy compared to other state-of-the-art approaches.
- left ventricle segmentation,
- the level set method,
- variational method,
- sparse shape prior
Citation: Wenyang Liu, Dan Ruan. Segmentation of Left Ventricle with a Coupled Length Regularization and Sparse Composite Shape Prior: a Variational Approach[J]. AIMS Medical Science, 2015, 2(4): 295-302. doi: 10.3934/medsci.2015.4.295

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Abstract

Segmentation of left ventricles in Cine MR images plays an important role in analyzing cardiac functions. In this study, we propose a variational method that incorporates both prior knowledge on geometrical coupling and shapes of the endo- and epicardium. Specifically, we dynamically maintain and update a smoothly varying distance between the endo- and epicardial contours, represented by a pair of level set functions, with a novel coupling energy embedded in the length regularization. We encode the shape prior with a sparse composite model based on a set of training templates. A robust fidelity with Gaussian mixture models is employed to provide robust intensity estimates in each subregion under insufficient local gradient information. Quantitative evaluation of the proposed method demonstrates competitive/better DSC and APD accuracy compared to other state-of-the-art approaches.

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