  Recent advancements in medical image segmentation techniques have achieved
compelling results. However, most of the widely used approaches do not take
into account any prior knowledge about the shape of the biomedical structures
being segmented. More recently, some works have presented approaches to
incorporate shape information. However, many of them are indeed introducing
more parameters to the segmentation network to learn the general features,
which any segmentation network is able learn, instead of specifically shape
features. In this paper, we present a novel approach that seamlessly integrates
the shape information into the segmentation network. Experiments on human brain
MRI segmentation demonstrate that our approach can achieve a lower Hausdorff
distance and higher Dice coefficient than the state-of-the-art approaches.
