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The Active Contour Model (ACM) is a standard image analysis technique whosenumerous variants have attracted an enormous amount of research attentionacross multiple fields. Incorrectly, however, the ACM'sdifferential-equation-based formulation and prototypical dependence on userinitialization have been regarded as being largely incompatible with therecently popular deep learning approaches to image segmentation. This paperintroduces the first tight unification of these two paradigms. In particular,we devise Deep Convolutional Active Contours (DCAC), a truly end-to-endtrainable image segmentation framework comprising a Convolutional NeuralNetwork (CNN) and an ACM with learnable parameters. The ACM's Eulerian energyfunctional includes per-pixel parameter maps predicted by the backbone CNN,which also initializes the ACM. Importantly, both the CNN and ACM componentsare fully implemented in TensorFlow, and the entire DCAC architecture isend-to-end automatically differentiable and backpropagation trainable withoutuser intervention. As a challenging test case, we tackle the problem ofbuilding instance segmentation in aerial images and evaluate DCAC on twopublicly available datasets, Vaihingen and Bing Huts. Our reseults demonstratethat, for building segmentation, the DCAC establishes a new state-of-the-artperformance by a wide margin.

End-to-End Deep Convolutional Active Contours for Image Segmentation