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Shape Constraints for the Left Ventricle Segmentation from Cardiac Cine MRI Based on Snake Models

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Shape Analysis in Medical Image Analysis

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 14))

Abstract

Segmentation of the left ventricle (LV) is a hot topic in cardiac magnetic resonance (MR) images analysis. To make thorough use of the anatomical and functional information, it is necessary to segment the endocardium and epicardium of the left ventricle. However, automatic and accurate segmentation of the left ventricle remains a challenging problem because of papillary muscles, lack of edge information, image inhomogeneity and low contrast at the epicardium. In this chapter, we address the shape constraints for extracting the endocardium and epicardium from cardiac cine MRI based on snake models. For endocardium segmentation, a circle/ellipse-based shape energy term is incorporated into the snake model. With this prior constraint, the snake contour can conquer the unexpected local minimum stemming from artifacts and papillary muscle. After extracting the endocardium, the edge map is modified to yield a new external force field for active contours, which automatically pushes the snake contour directly to the epicardium by employing the endocardium result as initialization. However, the circle constraint does not work very well for the epicardium, and the ellipse constraint needs the troublesome calculation of the ellipse orientation during snake evolution. Assuming that the epicardium resembles the endocardium in shape, we further propose a novel shape similarity energy for epicardium segmentation. With this energy, the snake model can avoid being trapped into artifacts and leaking out at weak boundaries. Based on the circle constraint and shape similarity energy, we present an automatic algorithm to extract the endocardium and epicardium of the LV simultaneously. Both qualitative and quantitative evaluations on our dataset and the publicly available database (e.g., MICCAI 2009) demonstrate the good performance of our algorithm.

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Acknowledgments

This work was supported in part by the Natural Science Foundation of China(NSFC) under Grants No.90920009 & No. 60602050, and the Key Program from the Tianjin Commission of Technology of China under Grant No. 11JCZDJC15600.

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Authors and Affiliations

  1. School of Computer Science, Tianjin University of Technology, Tianjin, 300384, People’s Republic of China

    Yuanquan Wang

  2. Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology (BIT), Beijing, 100081, People’s Republic of China

    Yuwei Wu & Yunde Jia

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  1. Yuanquan Wang
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  2. Yuwei Wu
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Correspondence to Yuanquan Wang .

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Editors and Affiliations

  1. GE Healthcare and University of Western Ontario, London, Ontario, Canada

    Shuo Li

  2. Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Wang, Y., Wu, Y., Jia, Y. (2014). Shape Constraints for the Left Ventricle Segmentation from Cardiac Cine MRI Based on Snake Models. In: Li, S., Tavares, J. (eds) Shape Analysis in Medical Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-03813-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-03813-1_12

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