2018
DOI: 10.1109/access.2017.2789179
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Deep Regression Segmentation for Cardiac Bi-Ventricle MR Images

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Cited by 40 publications
(23 citation statements)
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“…On this basis, a motion grid energy model is designed to extract and track the intersection of marker lines in cardiac motion image sequences. [ 16 , 17 ] The method can effectively segment the left ventricle, and the measurement of the image parameters has good reproducibility, and can overcome the noise in the cardiac magnetic resonance image and the influence of the surrounding tissue of the heart. It has good accuracy.…”
Section: Discussionmentioning
confidence: 99%
“…On this basis, a motion grid energy model is designed to extract and track the intersection of marker lines in cardiac motion image sequences. [ 16 , 17 ] The method can effectively segment the left ventricle, and the measurement of the image parameters has good reproducibility, and can overcome the noise in the cardiac magnetic resonance image and the influence of the surrounding tissue of the heart. It has good accuracy.…”
Section: Discussionmentioning
confidence: 99%
“…It was establish a framework to produce flow information and set of reference data to compare with unusual flow patterns due to cardiac abnormalities. In addition, Du propose a regression segmentation framework (Bi-DBN) to automatically segment bi-ventricle by establishing a boundary regression model that implicates the nonlinear mapping relationship between cardiac MR images and desired object boundaries [ 46 ]. Zhang propose meshfree particle computational method for cardiac image analysis with the energy minimization formulations to solve the fundamental problem about the optimal mathematical description in cardiac image analysis on a digital computer [ 47 ].…”
Section: Discussionmentioning
confidence: 99%
“…To achieve our goal, we adapted an image-based electromechanical model of the human ventricular heart from Johns Hopkins University [ 16 ]. Human ventricular geometries were generated using the methodology described by Gurev et al [ 17 20 ]. The three-dimensional human ventricular finite-element model used in this study consists of a lumped-parameter model of the physiological circulatory system [ 20 , 21 ].…”
Section: Methodsmentioning
confidence: 99%
“…Human ventricular geometries were generated using the methodology described by Gurev et al [ 17 20 ]. The three-dimensional human ventricular finite-element model used in this study consists of a lumped-parameter model of the physiological circulatory system [ 20 , 21 ]. Our ventricular model for the electrophysiological simulation consisted of 214,319 tetrahedral finite elements.…”
Section: Methodsmentioning
confidence: 99%