A Dual-space Patch-driven Complementary Learning Framework for Semi-supervised Multi-organ Segmentation

Semi-supervised medical image segmentation (SSMIS) aimsto achieve accurate segmentation with limited labeled data. However,existing methods still struggle in multi-organ scenarios. Limited anno-tations exacerbate class imbalance and feature entanglement among co-occurring organs, leading to false activations and performance degrada-tion. To overcome these challenges, we propose a dual-space patch-drivencomplementary learning framework for semi-supervised multi-organ seg-mentation (DPCL). Our framework addresses these problems from bothimage and feature space. In the image space, we combine uncertaintyand class frequency to form a probabilistic sampling strategy for im-age decomposition, which generates informative patches to enhance localfeature perception and promote balanced learning across organs. In thefeature space, we design two complementary objectives to achieve ro-bust feature disentanglement. The patch-based contrastive loss focuseson decoupling features of co-occurring neighboring organs within lo-cal patches, mitigating semantic confusion among adjacent structures.Meanwhile, the global-based contrastive loss minimizes the gap betweenglobal and local semantics, improving model sensitivity to fine-graineddetails and enhancing consistency across different regions. These com-plementary losses collaboratively enhance inter-organ feature discrimi-nation while mitigating false activations. Extensive evaluations on fourpublic multi-organ medical segmentation datasets (Synapse, FeTA2021,SegTHOR, and ACDC) demonstrate that DPCL outperforms state-of-the-art methods, achieving substantial improvements in segmentationperformance.