Supervised Learning for Segmenting Open Boundaries in Medical Images

Image segmentation is one of the most important building blocks in many medical imaging applications. Often, it is the first step in any artificial intelligence (AI) assisted diagnosis system. Most convolutional neural network image-to-image segmentation algorithms compute binary mask segmentation and extract the object boundary as the edge of the binary mask always leading to a closed boundary. In this paper, we present a novel image-to-image segmentation algorithm that learns open boundaries. The object delineation is directly learnt by training a U-Net like network on distance map representation of the boundary without any constraints on its shape or topology. We validate the proposed approach on the segmentation of the left atrium in intra-cardiac echocardiography images. For this application, it is important to produce segmentation only where a strong evidence of the anatomy exists. To our knowledge, this is the first work to train a U-Net on distance map ground truth representation for open boundary segmentation.