标题： 重新思考医学图像分割中的领域泛化：一张图像作为一个领域 显示英文标题

标题： Rethinking domain generalization in medical image segmentation: One image as one domain

摘要： 医学图像分割中的领域转移，尤其是在数据来自不同中心时，带来了重大挑战。 中心内部的差异，例如扫描仪型号或成像协议的不同，可能导致领域转移，其规模甚至可能大于或超过中心之间的领域转移。 为了解决这个问题，我们提出了“一张图像作为一个领域”（OIOD）假设，将每张图像视为一个独特的领域，从而实现灵活且稳健的领域泛化。 基于这一假设，我们开发了一个统一的解耦领域泛化（UniDDG）框架，能够在不需显式领域标签的情况下同时处理多源和单源领域泛化。 这种方法通过固定架构进行训练，与源领域的数量无关，降低了复杂性并提高了可扩展性。 我们将每个输入图像解耦为内容表示和风格代码，然后在批次内交换和组合这些内容以进行分割、重建和进一步的解耦。 通过为每张图像保持独立的风格代码，我们的模型确保了内容表示和风格代码的彻底解耦，提高了内容表示的领域不变性。 此外，我们通过扩展掩码注意力（EMA）增强泛化能力以保持边界，并通过风格增强（SA）模拟多样的图像风格，提高对领域转移的鲁棒性。 大量实验表明，我们的方法在视盘和视杯分割中分别实现了多源到单中心和单中心泛化的Dice分数为84.43%和88.91%，在前列腺分割中分别为86.96%和88.56%，优于当前最先进的领域泛化方法，在临床环境中表现出更优的性能和适应性。 显示英文摘要

摘要： Domain shifts in medical image segmentation, particularly when data comes from different centers, pose significant challenges. Intra-center variability, such as differences in scanner models or imaging protocols, can cause domain shifts as large as, or even larger than, those between centers. To address this, we propose the "one image as one domain" (OIOD) hypothesis, which treats each image as a unique domain, enabling flexible and robust domain generalization. Based on this hypothesis, we develop a unified disentanglement-based domain generalization (UniDDG) framework, which simultaneously handles both multi-source and single-source domain generalization without requiring explicit domain labels. This approach simplifies training with a fixed architecture, independent of the number of source domains, reducing complexity and enhancing scalability. We decouple each input image into content representation and style code, then exchange and combine these within the batch for segmentation, reconstruction, and further disentanglement. By maintaining distinct style codes for each image, our model ensures thorough decoupling of content representations and style codes, improving domain invariance of the content representations. Additionally, we enhance generalization with expansion mask attention (EMA) for boundary preservation and style augmentation (SA) to simulate diverse image styles, improving robustness to domain shifts. Extensive experiments show that our method achieves Dice scores of 84.43% and 88.91% for multi-source to single-center and single-center generalization in optic disc and optic cup segmentation, respectively, and 86.96% and 88.56% for prostate segmentation, outperforming current state-of-the-art domain generalization methods, offering superior performance and adaptability across clinical settings.