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ROPES: a semiautomated segmentation method for accelerated analysis of three-dimensional echocardiographic data

Echocardiography (cardiac ultrasound) is today the predominant technique for quantitative assessment of cardiac function and valvular heart lesions. Segmentation of cardiac structures is required to determine many important diagnostic parameters. As the heart is a moving organ, reliable information can be obtained only from three-dimensional (3-D) data over time (3-D + time = 4-D). Due to their size, the resulting four-dimensional (4-D) data sets are not reasonably accessible to simple manual segmentation methods. Automatic segmentation often yields unsatisfactory results in a clinical environment, especially for ultrasonic images. We describe a semiautomated segmentation algorithm (ROPES) that is able to greatly reduce the time necessary for user interaction and its application to extract various parameters from 4-D echocardiographic data. After searching for candidate contour points, which have to fulfill a multiscale edge criterion, the candidates are connected by minimizing a cost function to line segments that then are connected to form a closed contour. The contour is automatically checked for plausibility. If necessary, two correction methods that can also be used interactively are applied (fitting of other line segments into the contour and searching for additional candidates with a relaxed criterion). The method is validated using in vivo transesophageal echocardiographic data sets.