EIR-SDG: Explore Invariant Representation for Single-source Domain Generalization in Medical Image Segmentation

Single-source domain generalization (SDG) in medical image segmentation is a challenging yet practical task that efficiently enhances generalization ability while avoiding high annotation costs and privacy concerns. In this paper, we propose EIR-SDG, a novel SDG approach that explores domain-invariant representation for medical image segmentation. The core of EIR-SDG lies in mitigating the effect of style in the encoder while facilitating robust segmentation in the decoder. Concretely, we design a training-free texture and style diversity module that transforms images into diverse random appearances without requiring optimization or gradient updates, which simulates unseen target distributions while mitigating overfitting to regular patterns in synthetic data. Building on this, we devise a feature adaptive whitening module, which disentangles and whitens the style-sensitive feature correlations between original and augmented pairs, encouraging the encoder to learn invariant representations. Moreover, to facilitate robust segmentation in the decoder, a semantic representation optimization strategy is devised to enhance invariant representations by constraining the correlation between class prototypes to be consistent while improving segmentation boundary distinction by separating different class prototypes. Experiments on cross-modality abdominal, cross-sequence cardiac and cross-center prostate segmentation tasks demonstrate that our method achieves promising generalization capacity and outperforms the SOTA methods.