FIESTA：用于增强医学图像分割领域泛化能力的基于傅里叶的语义增强与不确定性引导方法
FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation
AI总结：
本文提出FIESTA方法，通过基于傅里叶的语义增强和不确定性引导，在频域中操纵幅度和相位，提升医学图像分割的单源域泛化能力，实验证明其性能优于现有方法。
摘要：
医学图像分割（MIS）中的单源域泛化（SDG）旨在仅利用单一源域的数据训练模型，以分割未见目标域的数据。尽管数据增强在SDG中取得了显著进展，但现有方法往往未能充分考虑MIS中普遍存在的细节和不确定区域，导致分割错误。本文提出了一种基于傅里叶的语义增强方法，称为FIESTA，利用不确定性引导，通过在频域中操纵幅度和相位分量来增强SDG背景下MIS的基本目标。所提出的傅里叶增强Transformer基于有意义的角度点进行语义幅度调制以诱发相关变化，并利用相位谱确保结构连贯性。此外，FIESTA采用认知不确定性来微调增强过程，提升模型适应多样化增强数据的能力，并使其聚焦于高模糊性区域。在三种跨域场景下的广泛实验表明，FIESTA在分割性能上超越了近期最先进的SDG方法，并显著提升了模型在医学成像模态中的适用性。
英文摘要：
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.
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