ToothFormer: A Region-Adaptive and Axis-Aware Transformer Network for Precise 3D CBCT Tooth Segmentation

Three-dimensional tooth segmentation plays a vital role in the diagnosis and treatment of dental diseases. However, due to the complex structure, significant regional heterogeneity, fuzzy boundaries, and the prevalence of small objects in CBCT images, existing methods face challenges in accuracy and robustness. This paper proposes a transformer-based approach, ToothFormer, designed to improve the segmentation accuracy of different tooth regions, fuzzy boundaries, and small objects. ToothFormer includes three key modules: the RAAM module (Region-Adaptive Attention Module), which implements region-adaptive attention allocation to improve segmentation consistency across different tooth regions; the DDRFM module (Dual-Domain Residual Fusion Module), which enhances boundary and small structure modeling through a detailed reconstruction path; and the DASM module (Dynamic Axis-aware Scale-fusion Module), which combines axial perception with a multi-scale attention mechanism to adapt to the anisotropic characteristics of CBCT images. Furthermore, this paper introduces the HAS module (Hybrid Attention Synergy) in the decoder for more effective fusion of local and global information. Experimental results on a public dental dataset demonstrate that ToothFormer significantly outperforms existing mainstream methods on multiple evaluation metrics, including Dice, Intersection over Union (IoU), and Average Segmented Distance (ASSD), validating the proposed method’s segmentation accuracy and robustness in complex dentition scenes.