Dental panoramic X-ray image segmentation for multi-feature coordinate position learning

Abstract

Objective To achieve an accurate assessment of orthodontic and restorative treatments, tooth segmentation of dental panoramic X-ray images is a critical preliminary step, however, dental panoramic X-ray images suffer from poorly defined interdental boundaries and low root-to-alveolar bone contrast, which pose significant challenges to tooth segmentation. In this article, we propose a multi-feature coordinate position learning-based tooth image segmentation method for tooth segmentation. Methods For better analysis, the input image is randomly flipped horizontally and vertically to enhance the data. Our method extracts multi-scale tooth features from the designed residual omni-dimensional dynamic convolution and the designed two-stream coordinate attention module can further complement the tooth boundary features, and finally the two features are fused to enhance the local details of the features and global contextual information, which achieves the enrichment and optimization of the feature information. Results The publicly available adult dental datasets Archive and Dataset and Code were used in the study. The experimental results were 87.96% and 92.04% for IoU, 97.79% and 97.32% for ACC, and 86.42% and 95.64% for Dice. Conclusion The experimental results show that the proposed network can be used to assist doctors in quickly viewing tooth positions, and we also validate the effectiveness of the proposed two modules in fusing features.