Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation

Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift problem for cross-modality medical segmentation. However, most of the existing UDA approaches depend on high-quality image translation with diversity constraints to explicitly augment the potential data diversity, which is hard to ensure semantic consistency and capture domain-invariant representation. In this paper, free of image translation and diversity constraints, we propose a novel Style Mixup Enhanced Disentanglement Learning (SMEDL) for UDA medical image segmentation to further improve domain generalization and enhance domain-invariant learning ability. Firstly, our method adopts disentangled style mixup to implicitly generate style-mixed domains with diverse styles in the feature space through a convex combination of disentangled style factors, which can effectively improve the model generalization. Meanwhile, we further introduce pixel-wise consistency regularization to ensure the effectiveness of style-mixed domains and provide domain consistency guidance. Secondly, we introduce dual-level domain-invariant learning, including intra-domain contrastive learning and inter-domain adversarial learning to mine the underlying domain-invariant representation under both intra- and inter-domain variations. We have conducted comprehensive experiments to evaluate our method on two public cardiac datasets and one brain dataset. Experimental results demonstrate that our proposed method achieves superior performance compared to the state-of-the-art methods for UDA medical image segmentation.