Structure-Guided Tooth Numbering and Lesion Localization in Visible Light Oral Images

This study presents a structure-aware inference framework for tooth numbering and lesion localization in visible light oral images. Tooth numbering is often compromised by class imbalance and structural inconsistency caused by the uneven distribution of tooth types, motivating the integration of anatomical priors into the inference process. The framework first partitions the dental arch into quadrants using a deep learning-based detection module to establish spatial organization. Based on this, an Anchor-Teeth-Guided Inference (ATGI) strategy reconstructs globally consistent tooth numbering by leveraging dental arch continuity, bilateral symmetry, and confidence-guided anchor selection, thereby improving the recognition of underrepresented tooth classes. Visually suspicious lesion regions are independently detected and spatially associated with numbered teeth, enabling joint structural and lesion-aware analysis. Evaluated on a multi-source dataset, the method achieves a weighted F1-score of 0.813 for 32-class tooth numbering, outperforming end-to-end baselines while improving spatial consistency. Lesion localization yields F1-scores of 0.850 for caries-related regions and 0.789 for gingivitis-related regions. These results demonstrate that incorporating anatomical constraints enhances numbering robustness and improves rare-class recognition in visible light dental image analysis, showing potential for screening-oriented oral assessment and teledentistry applications.