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Due to their general and robust formulation deformable models offer a very appealing approach to 3D image segmentation. However there is a trade-off between model genericity, model accuracy and computational efficiency. In general, fully generic models require a uniform sampling of either the space or their mesh. The segmentation accuracy is thus a global parameter. Recovering small image features results in heavy computational costs whereas generally only restricted parts of images require a high segmentation accuracy. We present a highly deformable model that both handles fully automated topology changes and adapts its resolution locally according to the geometry of image features. The main idea is to replace the Euclidean metric with a Riemannian metric that expands interesting parts of the image. Then, a regular sampling is maintained with this new metric. This allows to automatically handle topology changes while increasing the model resolution locally according to the geometry of image components. By this way high quality segmentation is achieved with reduced computational costs.