Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation

• Published in: arXiv.org 2022-06
• Main Authors: Zhang, Zhenxi, Tian, Chunna, Jiao, Zhicheng
• Format: Article
• Language: English
• Subjects: Alignment; Annotations; Consistency; Datasets; Image classification; Image segmentation; Medical imaging; Prototypes
• ISSN: 2331-8422

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.