Deep Learning Based Generalization of Chan-Vese Level Sets Segmentation

Chan-Vese is a level set method that simultaneously evolves a level set surface and fits locally constant intensity models for the interior and exterior regions to minimize a Mumford-Shah integral. However, the length-based contour regularization in the Chan-Vese formulation is quite simple and too weak for many applications. In this dissertation we introduce a generalization of the Chan-Vese method to evolve a curve where the regularization is based on a Fully Convolutional Neural Network. We also show how to learn the curve model as a Recurrent Neural Network (RNN) using training examples. Our RNN differs from the standard ones because it has the Chan-Vese locally constant intensity model, which gives it better interpretability and flexibility.