Pair Shuffle Consistency for Semi-supervised Medical Image Segmentation

Semi-supervised medical image segmentation is a practical but challenging problem, in which only limited pixel-wise annotations are available for training. While most existing methods train a segmentation model by using the labeled and unlabeled data separately, the learning paradigm solely based on unlabeled data is less reliable due to the possible incorrectness of pseudo labels. In this paper, we propose a novel method namely pair shuffle consistency (PSC) learning for semi-supervised medical image segmentation. The pair shuffle operation splits an image pair into patches, and then randomly shuffle them to obtain mixed images. With the shuffled images for training, local information is better interpreted for pixel-wise predictions. The consistency learning of labeled-unlabeled image pairs becomes more reliable, since predictions of the unlabeled data can be learned from those of the labeled data with ground truth. To enhance the model robustness, the consistency constraint on unlabeled-unlabeled image pairs serves as a regularization term, thereby further improving the segmentation performance. Experiments on three benchmarks demonstrate that our method outperforms the state of the art for semi-supervised medical image segmentation.