  Semantic segmentation is an important preliminary step towards automatic
medical image interpretation. Recently deep convolutional neural networks have
become the first choice for the task of pixel-wise class prediction. While
incorporating prior knowledge about the structure of target objects has proven
effective in traditional energy-based segmentation approaches, there has not
been a clear way for encoding prior knowledge into deep learning frameworks. In
this work, we propose a new loss term that encodes the star shape prior into
the loss function of an end-to-end trainable fully convolutional network (FCN)
framework. We penalize non-star shape segments in FCN prediction maps to
guarantee a global structure in segmentation results. Our experiments
demonstrate the advantage of regularizing FCN parameters by the star shape
prior and our results on the ISBI 2017 skin segmentation challenge data set
achieve the first rank in the segmentation task among $21$ participating teams.
