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

• URL: http://arxiv.org/abs/2206.01739v1
• Date: Fri, 3 Jun 2022 02:59:22 GMT
• Title: Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation
• Authors: Zhenxi Zhang, Chunna Tian, Zhicheng Jiao
• Abstract summary: 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. Our method also outperforms seven state-of-the-art semi-supervised segmentation methods on all three datasets.
• Score: 5.426994893258762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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