Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts. Although semi-supervised methods can improve the performance by utilizing unlabeled data, there are still gaps between fully supervised methods under extremely limited annotation scenarios. In this paper, we propose a simple yet efficient strategy to explore the usage of the Segment Anything Model (SAM) for enhancing semi-supervised medical image segmentation. Concretely, the segmentation model trained with domain knowledge provides information for localization and generating input prompts to the SAM. Then the generated pseudo-labels of SAM are utilized as additional supervision to assist in the learning procedure of the semi-supervised framework. Experimental results demonstrate that SAM's assistance significantly enhances the performance of existing semi-supervised frameworks, especially when only one or a few labeled images are available.

翻译：摘要：半监督学习因其相较于全监督方法减少了对专家密集标注的依赖而备受关注，这在通常需要领域专家逐像素/体素标注的医学图像分割任务中尤为重要。尽管半监督方法可通过利用未标注数据提升性能，但在标注极度匮乏的场景下，其与全监督方法之间仍存在差距。本文提出一种简洁高效的策略，探索分割一切模型（Segment Anything Model, SAM）在增强半监督医学图像分割中的应用。具体而言，基于领域知识训练的分割模型提供定位信息并生成SAM的输入提示；随后，SAM生成的伪标签被用作额外监督信号，辅助半监督框架的学习过程。实验结果表明，SAM的辅助能显著提升现有半监督框架的性能，尤其在仅有一张或少数几张标注图像可用时效果尤为突出。