A Reliable Multi-Stage System for Tooth Instance Segmentation and Numbering in Panoramic Radiographs

Accurate tooth instance segmentation and identification in panoramic radiographs is a fundamental prerequisite for automated dental diagnosis and treatment planning. Existing methods often rely on static segmentation pipelines that overlook anatomical symmetry and fail to address structural anomalies such as supernumerary or missing teeth. To this end, we propose a reliable multi-stage framework for clinically applicable tooth instance segmentation and FDI-compliant numbering. The system comprises three stages: a supernumerary tooth classifier to guide adaptive routing, a core instance segmentor for tooth-level delineation and numbering, and a missing tooth detector to ensure index completeness via post-hoc correction. Central to our design is the Symmetry-Aware Dual-branch Pyramid Network (SADP-Net), which explicitly models bilateral structures and scale variations through a Symmetry-Aware Module (SymAM) and a Dual-Branch Pyramid (DBP) architecture. Extensive experiments on three datasets, including Tufts Dental, O²PR, and a multi-pathology cohort, demonstrate superior performance compared to state-of-the-art baselines. Ablation studies further validate the contributions of each proposed component in enhancing boundary localization, numbering consistency, and robustness to anatomical variability. Our framework provides a scalable and interpretable solution for real-world dental imaging systems. Codes link: https://github.com/qklee-lz/SADP-Net.