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Extraction of structures of interest in medical images is often an arduous task because of noisy or incomplete data. However, hand- segmented data are often available and most of the structures to be extracted have a similar shape from one subject to an other. Then, the possibility of modeling a family of shapes and restricting the new struc- ture to be extracted within this class is of particular interest. This ap- proach is commonly implemented using active shape models (2) and the definition of the image term is the most challenging component of such an approach. In parallel, level set methods (8) define a powerful optimiza- tion framework, that can be used to recover objects of interest by the propagation of curves or surfaces. They can support complex topologies, considered in higher dimensions, are implicit, intrinsic and parameter free. In this paper we re-visit active shape models and introduce a level set variant of them. Such an approach can account for prior shape knowl- edge quite eciently as well as use data/image terms of various form and complexity. Promising results on the extraction of brain ventricles in MR images demonstrate the potential of our approach.

Implicit Active Shape Models for 3D Segmentation in MR Imaging