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The segmentation of the left ventricle (LV) in echocardiographic data has proven itself a useful methodology to assess cardiac function and to detect abnormalities. Traditionally, cardiologists segment the LV border at the end-systolic and end-diastolic phases to determine the ejection fraction. However, the manual segmentation of the LV is a tedious and time demanding task, which means automated segmentation systems can provide a powerful tool to improve workflow in a clinical setup. This paper proposes a robust 3D segmentation system consisting of a deformable model that uses a probabilistic data association filter (PDAF) to robustly detect the LV border. Results show that the algorithm performs well in both synthetic and real data, without significantly compromising its performance. The obtained LV segmentations are compared with the manual segmentations performed by an expert, yielding an average distance of 4 pixel between points from both segmentations.

ROBUST DEFORMABLE MODEL FOR SEGMENTING THE LEFT VENTRICLE IN 3D VOLUMES OF ULTRASOUND DATA