Real-time three-dimensional (RT3D) echocardiography is a new image acquisition technique that allows instantaneous acquisition of volumetric images for quantitative assessment of cardiac morphology and function. To quantify many important diagnostic parameters, such as ventricular volume, ejection fraction, and cardiac output, an automatic algorithm to delineate the left ventricle (LV) from RT3D echocardiographic images is essential. While a number of efforts have been made towards segmentation of the LV endocardial (ENDO) boundaries, the segmentation of epicardial (EPI) boundaries remains problematic. In this paper, we present a coupled deformable model that addresses this problem. The idea behind our method is that the volume of the myocardium is close to being constant during a cardiac cycle and our model uses this coupling as an important constraint. We employ two surfaces, each driven by the image-derived information that takes into account ultrasound physics by modeling the speckle statistics using the Nakagami distribution while maintaining the coupling. By simultaneously evolving two surfaces, the final segmentation of the myocardium is thus achieved. Results from 80 sets of synthetic data and 286 sets of real canine data were evaluated against the ground truth and against outlines from three independent observers, respectively. We show that results obtained with our incompressibility constraint were more accurate than those obtained without constraint or with a wall thickness constraint, and were comparable to those from manual segmentation.
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