Pseudo Label-based Multi-View Annotation for Semi-Supervised Medical Image Segmentation

Semi-supervised learning (SSL) provides an effective solution to the problem of requiring time-consuming annotations in medical image segmentation. In this work, we propose a novel semi-supervised learning network(PMVA) based on multi-view annotation for medical image segmentation. Specifically, we propose the multi-view annotation by only labeling three multi-view slices in a labeled volume, which significantly relieves the burden of annotation. Then, we perform registration to obtain the initial pseudo labels for sparsely labeled volumes. Subsequently, by introducing unlabeled volumes, we propose a triplet-network paradigm that exploits dense pseudo labels in the early stage and sparse labels in the later stage and meanwhile forces the consistent output of three networks. Finally, the experimental results both on the medical data of Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) in the open clinical library demonstrate that our method can generate effective pseudo labels from the labeled medical images to the unlabeled ones efficiently. Compared with existing algorithms, comprehensive quantitative and qualitative evaluations have shown excellent segmentation performance of our method.