MDCL-UNet: A Multi-Domain Collaborative Learning Method for Medical Image Segmentation

Hu Xu, Chaobin Wang, Junni Huang, Meijun Sun, Jinchang Ren, Zheng Wang

Medical image segmentation, which delineates anatomical structures and pathological regions at the pixel level, plays an important role in computer-aided diagnosis. Existing segmentation methods are typically developed and evaluated under a single-dataset setting, and their performance often degrades when applied to images from different medical centers, scanners, or acquisition protocols due to substantial domain gaps. While cross-dataset collaborative learning methods can train a unified model from multiple datasets, they generally do not explicitly distinguish domain-specific appearance variations from domain-invariant semantic information, placing a heavy burden on shared layers when the domain gap is large. To address this issue, we propose MDCL-UNet, a supervised multi-domain collaborative learning framework based on domain feature disentanglement. MDCL-UNet adopts a two-branched encoder in which a domain-specific branch equipped with the proposed Domain Style Instance Normalization (DSIN) module and a domain-invariant branch jointly disentangle domain features. A domain adversarial classifier and an MMD-based domain decoupling loss are used to ensure that the two branches learn complementary and orthogonal representations. Instead of discarding domain-specific features as in conventional domain generalization methods, a Domain Fusion Attention Module (DFAM) is further introduced to adaptively fuse domain-specific style cues with domain-invariant semantic features through attention-based integration, enabling effective reuse of cross-domain information. Extensive experiments on retinal vessel, optic disc/cup, and abdominal multi-organ segmentation datasets demonstrate that MDCL-UNet achieves consistently higher Dice scores and lower HD95 distances than single-dataset baselines and existing cross-dataset collaborative learning methods. Moreover, MDCL-UNet maintains stable training and favorable scalability with increasing numbers of domains. The source code will be available at https://github.com/lqr41710085/MDCL-UNET .