SPNet: Shape Prediction Using a Fully Convolutional Neural Network

Shape has widely been used in medical image segmentation algorithms to constrain a segmented region to a class of learned shapes. Recent methods for object segmentation mostly use deep learning algorithms. The state-of-the-art deep segmentation networks are trained with loss functions defined in a pixel-wise manner, which is not suitable for learning topological shape information and constraining segmentation results. In this paper, we propose a novel shape predictor network for object segmentation. The proposed deep fully convolutional neural network learns to predict shapes instead of learning pixel-wise classification. We apply the novel shape predictor network to X-ray images of cervical vertebra where shape is of utmost importance. The proposed network is trained with a novel loss function that computes the error in the shape domain. Experimental results demonstrate the effectiveness of the proposed method to achieve state-of-the-art segmentation, with correct topology and accurate fitting that matches expert segmentation.