Dynamic domain generalization for medical image segmentation

Abstract

Domain Generalization-based Medical Image Segmentation (DGMIS) aims to enhance the robustness of segmentation models on unseen target domains by learning from fully annotated data across multiple source domains. Despite the progress made by traditional DGMIS methods, they still face several challenges. First, most DGMIS approaches rely on static models to perform inference on unseen target domains, lacking the ability to dynamically adapt to samples from different target domains. Second, current DGMIS methods often use Fourier transforms to simulate target domain styles from a global perspective, but relying solely on global transformations for data augmentation fails to fully capture the complexity and local details of the target domains. To address these issues, we propose a Dynamic Domain Generalization (DDG) method for medical image segmentation, which improves the generalization capability of models on unseen target domains by dynamically adjusting model parameters and effectively simulating target domain styles. Specifically, we design a Dynamic Position Transfer (DPT) module that decouples model parameters into static and dynamic components while incorporating positional encoding information to enable efficient feature representation and dynamic adaptation to target domain characteristics. Additionally, we introduce a Global-Local Fourier Random Transformation (GLFRT) module, which jointly considers both global and local style information of the samples. By using a random style selection strategy, this module enhances sample diversity while controlling training costs. Experimental results demonstrate that our method outperforms state-of-the-art approaches on several public medical image datasets, achieving average Dice score improvements of 0.58%, 0.76%, and 0.76% on the Fundus dataset (1060 retinal images), Prostate dataset (1744 T2-weighted MRI scans), and SCGM dataset (551 MRI image slices), respectively. The code is available online (https://github.com/ZMC-IIIM/DDG-Med).