24 May 2017 Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces
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With the advancement of three-dimensional (3-D) real-time echocardiography in recent years, automatic creation of patient specific geometric models is becoming feasible and important in clinical decision making. However, the vast majority of echocardiographic segmentation methods presented in the literature focus on the left ventricle (LV) endocardial border, leaving segmentation of the right ventricle (RV) a largely unexplored problem, despite the increasing recognition of the RV’s role in cardiovascular disease. We present a method for coupled segmentation of the endo- and epicardial borders of both the LV and RV in 3-D ultrasound images. To solve the segmentation problem, we propose an extension of a successful state-estimation segmentation framework with a geometrical representation of coupled surfaces, as well as the introduction of myocardial incompressibility to regularize the segmentation. The method was validated against manual measurements and segmentations in images of 16 patients. Mean absolute distances of 2.8 ± 0.4    mm , 3.2 ± 0.7    mm , and 3.1 ± 0.5    mm between the proposed and reference segmentations were observed for the LV endocardium, RV endocardium, and LV epicardium surfaces, respectively. The method was computationally efficient, with a computation time of 2.1 ± 0.4    s .
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jørn Bersvendsen, Jørn Bersvendsen, Fredrik Orderud, Fredrik Orderud, Øyvind Lie, Øyvind Lie, Richard John Massey, Richard John Massey, Kristian Fosså, Kristian Fosså, Raúl San José Estépar, Raúl San José Estépar, Stig Urheim, Stig Urheim, Eigil Samset, Eigil Samset, } "Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces," Journal of Medical Imaging 4(2), 024005 (24 May 2017). https://doi.org/10.1117/1.JMI.4.2.024005 . Submission: Received: 27 January 2016; Accepted: 1 May 2017
Received: 27 January 2016; Accepted: 1 May 2017; Published: 24 May 2017

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