Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation
This work addresses the challenge of semi-supervised segmentation for medical imaging, where labeled data is limited, and it is incremental by building on prototype alignment methods.
The paper tackles the problem of scarce pixel-level annotations in medical image segmentation by proposing a mutual- and self-prototype alignment framework to better utilize unlabeled data, achieving large improvements and outperforming seven state-of-the-art methods on three datasets.
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.