Safe Generalization: Mitigating Catastrophic Forgetting in Single-Source Multi-Organ Segmentation via Collaborative Causal Learning

Single-Source Domain Generalization (SDG) in medical multi-organ image segmentation confronts severe challenges of distributionshifts caused by varying imaging protocols. However, we identify a crit-ical yet overlooked phenomenon in current methodologies: CatastrophicForgetting in SDG (CF-SDG). Specifically, existing generalization strate-gies often lack explicit protection for source domain features, leading toperformance degradation on the source domain while improving general-ization capabilities. To address this, we propose the Collaborative CausalLearning Network (CCL-Net), a unified framework that simultaneouslyachieves robust generalization and source knowledge preservation. Froma causal perspective, we construct a dual-path causal intervention mech-anism with source domain knowledge constraints. First, we design anOrthogonal Structure Disentanglement (OSD) module to learn struc-tural mediator representations, establishing a pure structural pathwayby blocking the intrusion of non-causal information via orthogonaliza-tion strategies. Second, we introduce an Anatomy-Guided Local CausalIntervention (AG-LCI) module to physically sever the spurious corre-lations between anatomical semantics and local appearances throughmask-guided counterfactual generation. Furthermore, to mitigate CF-SDG, a novel Collaborative Learning Constraint mechanism is designedto anchor source domain memory via dynamic local-global consistencyregularization. Extensive experiments on multi-modality and multi-organdatasets demonstrate that CCL-Net not only achieves state-of-the-artgeneralization performance on unseen domains but also maintains a sig-nificantly lower forgetting rate on the source domain.