LGMUNet: Global-local synergy of Mamba and CNN for dental X-ray segmentation

Background Panoramic dental X-ray segmentation is critical clinically. However, inherent challenges—low contrast, blurred boundaries, complex root apices, and variable wisdom teeth—hinder pure CNN or Transformer models from effectively extracting global structures and local fine-grained features. Objective LGMUNet is proposed, a dual-branch framework seamlessly integrating Mamba's long-range dependency modeling with CNN's local fine-grained extraction. This global-local synergy not only tackles the segmentation difficulties of complex dental topologies but also eliminates the semantic gaps inherent in conventional hybrid architectures. Methods LGMUNet employs dynamic convolutional stems in shallow layers to extract local textures. In deep layers, the LGM module first aggregates channel information through an MLP, then feeds into a parallel dual-branch architecture: the Mamba branch models long-range dependencies via cross-scanning, and the DwConv branch captures local high-frequency details, with both branches interacting at same feature depth. The model was evaluated on the multi-source STDS and MICCAI 2023 datasets. Results Independent repeat trials confirm that LGMUNet achieves state-of-the-art performance. On the STDS and MICCAI 2023 datasets, it achieves Dice coefficients of 92.64 ± 0.31% and 94.00 ± 0.19%, outperforming models like nnUNet and TMamba. It demonstrates superior accuracy and clinical robustness when addressing highly challenging cases, including impacted wisdom teeth and ambiguous boundaries.