Sparse Annotation Deep Learning for Prostate Segmentation of Volumetric Magnetic Resonance Images

Deep neural networks (DNNs) have achieved unprecedented performances in various medical image segmentation tasks. Nevertheless, DNN training requires a large amount of densely labeled data, which are labor-intensive and time-consuming to obtain. Here, we address the task of segmenting volumetric MR images using extremely sparse annotations, for which only the central slices are labeled manually. In our framework, two independent sets of pseudo labels are generated for unlabeled slices using self-supervised and semi-supervised learning methods. Boolean operation is adopted to achieve robust pseudo labels. Our approach can be very important in clinical applications to reduce manual effort on dataset construction.