Within the wide field of medical imaging research, image segmentation is one of the earliest but still open topics. This thesis focuses on model-based segmentation methods, which achieve a good trade-off between genericity and ability to carry prior information on the target organ. Our goal is to build an efficient segmentation framework that is able to leverage all kinds of external information, i.e. annotated databases via statistical learning, other images from the patient via co-segmentation and user input via live interactions. This work is based on the implicit template deformation framework, a variational method relying on an implicit representation of shapes. After improving the mathematical formulation of this approach, we show its potential on challenging clinical problems. Then, we introduce different generalizations, all independent but complementary, aimed at enriching both the shape and appearance model exploited. The diversity of the clinical applications addressed shows the genericity and the effectiveness of our contributions.
