Recent works have indicated the potential of using curvature as a regularizer in image segmentation, in particular for the class of thin and elongated objects. These are ubiquitous in biomedical imaging (e.g. vascular networks), in which length regularization can sometime perform badly, as well as in texture identification. However, curvature is a second-order differential measure, and so its estimators are sensitive to noise. State-of-art techniques make use of a coarse approximation of curvature that limits practical applications. In this talk I propose the use of multigrid convergent estimators instead, and I will show a new digital curvature flow derived from it that mimics continuous curvature flow. Finally, an application as a post-processing step to a variational segmentation framework is presented.