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

Wenyang Liu; Dan Ruan

doi:10.3934/medsci.2015.4.295

AIMS Medical Science, 2015, 2(4): 295-302. doi: 10.3934/medsci.2015.4.295. Research article Special Issues

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

Wenyang Liu, Dan Ruan

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 date: , Accepted date: , Published date:

Special Issues: Data integration and high dimensional statistics in Medical Imaging

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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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Keywords: 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. AIMS Medical Science, 2015, 2(4): 295-302. doi: 10.3934/medsci.2015.4.295

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This article has been cited by:

1. Wenyang Liu, Yam Cheung, Amit Sawant, Dan Ruan, A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, Medical Physics, 2016, 43, 5, 2353, 10.1118/1.4945695

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Copyright Info: 2015, Wenyang Liu, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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