P2A-6 Automatic Segmentation of the Left Ventricle in 3D Echocardiography Using Active Appearance Models

Assessment of left ventricular (LV) functional parameters, such as LV volume, ejection fraction and stroke volume, from real-time 3D echocardiography (RT3DE) is labor intensive and subjective, because in current analyses it requires input from the user. Automating these procedures will save valuable time in the analysis and will remove interobserver variability. We propose a fully automatic segmentation approach for the left ventricle in real-time 3D echocardiography, based on active appearance models (AAMs), using ultrasound specific grey value normalization. We evaluated shape and texture model generalization. Also, automatic segmentation has been preliminarily evaluated on transthoracic, apical acquisitions of 54 patients, acquired with the fast rotating ultrasound (FRU-) transducer (18 patients) and with the Philips Sonos 7500 (36 patients). The evaluations were done in a leave-N-out manner (with N=5). We evaluated point-to-surface (P2S) distances for the segmented endocardial contours to the expert manual contours. The generalization of the shape model was good, but texture model generalization was moderate, hampering the AAM matching We found preliminary segmentation errors (P2S) of 3.9plusmn 1.6 mm (N=54) for detection using AAM matching These results indicate that fully automatic segmentation of the LV in RT3DE using AAMs is feasible.