MambaMatch: A Novel Model for Semi-Supervised Spinal Cortical and Cancellous Bone Segmentation

Precise cortical and cancellous bone segmentation is essential for safe laminectomy. However, it remains challenging due to limited annotations and high anatomical similarity. To mitigate these challenges, we propose MambaMatch, an end-to-end semi-supervised segmentation framework collaboratively optimized under a teacher–student paradigm. The student branch adopts an innovative Mamba-ASPP-Unet (MAU) module, which integrates multi-scale Atrous Spatial Pyramid Pooling (ASPP) with channel and spatial attention to actively extract global spinal structures and cortical boundary features, while the teacher branch guides the student through constraints imposed by the KL-divergence. To enhance feature diversity, a dual-stream perturbation strategy is employed, combining Correlation-Guided CutMix Augmentation (CGCA) on high-response regions with standard strong augmentations. Furthermore, to account for inter-class variations in pseudo-label reliability, dynamic thresholding and temperature scaling are further introduced to adaptively balance pseudo-label selection and the intensity of consistency loss. MambaMatch achieves an mIoU of 79.69% and a Dice of 83.57% on our clinically validated CT Cortical– Cancellous Dataset, and also shows strong robustness on MRI-SPIDER, SKIN-ISIC 2018, PH², and CT-VerSe datasets and the model remains lightweight and efficient. These results show that MambaMatch provides an efficient and accurate segmentation framework with clear potential to support clinical workflows in spinal surgery, while also demonstrating value across a broader range of clinical imaging scenarios.