Single-source domain generalization (SDG) in medical image segmentation (MIS) aims to generalize a model using data from only one source domain to segment data from an unseen target domain. Despite substantial advances in SDG with data augmentation, existing methods often fail to fully consider the details and uncertain areas prevalent in MIS, leading to mis-segmentation. This paper proposes a Fourier-based semantic augmentation method called FIESTA using uncertainty guidance to enhance the fundamental goals of MIS in an SDG context by manipulating the amplitude and phase components in the frequency domain. The proposed Fourier augmentative transformer addresses semantic amplitude modulation based on meaningful angular points to induce pertinent variations and harnesses the phase spectrum to ensure structural coherence. Moreover, FIESTA employs epistemic uncertainty to fine-tune the augmentation process, improving the ability of the model to adapt to diverse augmented data and concentrate on areas with higher ambiguity. Extensive experiments across three cross-domain scenarios demonstrate that FIESTA surpasses recent state-of-the-art SDG approaches in segmentation performance and significantly contributes to boosting the applicability of the model in medical imaging modalities.

翻译：医学图像分割中的单源域泛化旨在利用仅来自单一源域的数据训练模型，以分割来自未见目标域的数据。尽管基于数据增强的SDG方法已取得显著进展，现有方法往往未能充分考虑医学图像分割中普遍存在的细节与不确定区域，从而导致误分割。本文提出一种基于傅里叶变换的语义增强方法FIESTA，该方法通过频域中的振幅与相位分量操作，在SDG框架下利用不确定性引导来增强医学图像分割的核心目标。所提出的傅里叶增强变换器基于有意义的角点实现语义振幅调制以引入相关变异，并利用相位谱确保结构一致性。此外，FIESTA采用认知不确定性对增强过程进行微调，从而提升模型适应多样化增强数据的能力，并使其更专注于具有较高模糊性的区域。在三种跨域场景下的大量实验表明，FIESTA在分割性能上超越了当前最先进的SDG方法，并对提升模型在医学影像模态中的适用性作出了显著贡献。