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In the automatic segmentation of echocardiographic images, a priori shape knowledge has been used to compensate for poor features in ultrasound images. This shape knowledge is often learned via an off-line training process, which requires tedious human effort and is highly expertise-dependent. More importantly, a learned shape template can only be used to segment a specific class of images with similar boundary shape. In this paper, we present a multi-scale level set framework for segmentationdoi:10.1016/s1361-8415(03)00035-5 pmid:14561556 fatcat:n5ez2odvwnhj7lrwgkushkn3ly