Deep learning-based medical image segmentation models suffer from performance degradation when deployed to a new healthcare center. To address this issue, unsupervised domain adaptation and multi-source domain generalization methods have been proposed, which, however, are less favorable for clinical practice due to the cost of acquiring target-domain data and the privacy concerns associated with redistributing the data from multiple source domains. In this paper, we propose a \textbf{C}hannel-level \textbf{C}ontrastive \textbf{S}ingle \textbf{D}omain \textbf{G}eneralization (\textbf{C$^2$SDG}) model for medical image segmentation. In C$^2$SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations. The segmentation is performed based solely on the structure representations. Our method is novel in the contrastive perspective that enables channel-wise feature disentanglement using a single source domain. We evaluated C$^2$SDG against six SDG methods on a multi-domain joint optic cup and optic disc segmentation benchmark. Our results suggest the effectiveness of each module in C$^2$SDG and also indicate that C$^2$SDG outperforms the baseline and all competing methods with a large margin. The code will be available at \url{https://github.com/ShishuaiHu/CCSDG}.

翻译：基于深度学习的医学图像分割模型在部署到新的医疗机构时会出现性能下降。为解决这一问题，研究人员提出了无监督域适应和多源域泛化方法，但这些方法因获取目标域数据的成本以及多个源域数据重新分发所涉及的隐私问题，在临床实践中并不理想。本文提出了一种**通道级对比性单域泛化**（C$^2$SDG）模型用于医学图像分割。在C$^2$SDG中，每幅图像及其风格增强版本的浅层特征被提取并用于对比训练，从而分离出风格表征和结构表征。分割过程仅基于结构表征进行。我们的方法在对比学习视角上具有创新性，能够利用单个源域实现通道级特征分离。我们在多域视杯与视盘联合分割基准上，将C$^2$SDG与六种单域泛化方法进行了评估。结果表明，C$^2$SDG中每个模块均具有有效性，并且C$^2$SDG在性能上大幅优于基线方法及所有对比方法。代码将在\url{https://github.com/ShishuaiHu/CCSDG}提供。