  Semi-supervised learning methods have been explored in medical image
segmentation tasks due to the scarcity of pixel-level annotation in the real
scenario. Proto-type alignment based consistency constraint is an intuitional
and plausible solu-tion to explore the useful information in the unlabeled
data. In this paper, we propose a mutual- and self- prototype alignment (MSPA)
framework to better utilize the unlabeled data. In specific, mutual-prototype
alignment enhances the information interaction between labeled and unlabeled
data. The mutual-prototype alignment imposes two consistency constraints in
reverse directions between the unlabeled and labeled data, which enables the
consistent embedding and model discriminability on unlabeled data. The proposed
self-prototype alignment learns more stable region-wise features within
unlabeled images, which optimizes the classification margin in semi-supervised
segmentation by boosting the intra-class compactness and inter-class separation
on the feature space. Extensive experimental results on three medical datasets
demonstrate that with a small amount of labeled data, MSPA achieves large
improvements by leveraging the unlabeled data. Our method also outperforms
seven state-of-the-art semi-supervised segmentation methods on all three
datasets.
