Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to disentangle domain-specific and domain-invariant features is key for achieving Domain Generalization (DG). However, existing DG methods can hardly achieve effective disentanglement to get high generalizability. To deal with this problem, we propose an efficient Contrastive Domain Disentanglement and Style Augmentation (CDDSA) framework for generalizable medical image segmentation. First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image. Second, to achieve better disentanglement, a contrastive loss is proposed to encourage the style codes from the same domain and different domains to be compact and divergent, respectively. Thirdly, to further improve generalizability, we propose a style augmentation method based on the disentanglement representation to synthesize images in various unseen styles with shared anatomical structures. Our method was validated on a public multi-site fundus image dataset for optic cup and disc segmentation and an in-house multi-site Nasopharyngeal Carcinoma Magnetic Resonance Image (NPC-MRI) dataset for nasopharynx Gross Tumor Volume (GTVnx) segmentation. Experimental results showed that the proposed CDDSA achieved remarkable generalizability across different domains, and it outperformed several state-of-the-art methods in domain-generalizable segmentation.
翻译:以潜在域变换和不同风格的先前看不见的图像进行概括化,对于临床上适用的医学图像分解至关重要,而将图像分解为部域异变和域异性功能的能力是实现DG通用(DG)的关键。然而,现有的DG方法很难实现有效的分解,以获得高共性。为了解决这个问题,我们建议为普通化医学图像分解建立一个高效的对比多相异和样式增强(CDDSA)框架。首先,建议建立一个分解网络,将图像分解成一个域异异异解解解解异异异的解异性解剖和域异异性结构,而将图像分解解成一个域异异异异异异的正异性解异性表示。 第三,将前者发送到一个不受域变异异异异性剖面的离异性结构,我们提议在多级变异性变异性变异性变异性数据结构上显示一个共性变异性数据结构。