  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.
