Template-based semantic-guided orthodontic teeth alignment previewer

Abstract

Intuitive visualization of orthodontic prediction results is of great significance in helping patients make up their minds about orthodontics and maintain an optimistic attitude during treatment. To address this, we propose a semantically guided orthodontic simulation prediction framework that predicts orthodontic outcomes using only a frontal photograph. Our method comprises four key steps. Firstly, we perform semantic segmentation of oral and the teeth cavity, enabling the extraction of category-specific tooth contours from frontal images with misaligned teeth. Secondly, these extracted contours are employed to adapt the predefined teeth templates to reconstruct 3D models of the teeth. Thirdly, using the reconstructed tooth positions, sizes, and postures, we fit the dental arch curve to guide tooth movement, producing a 3D model of the teeth after simulated orthodontic adjustments. Ultimately, we apply a semantically guided diffusion model for structural control and generate orthodontic prediction images which are consistent with the style of input images by applying texture transformation. Notably, our tooth semantic segmentation model attains an average intersection of union of 0.834 for 24 tooth classes excluding the second and third molars. The average Chamfer distance between our reconstructed teeth models and their corresponding ground-truth counterparts measures at 1.272 mm${}^2$ in test cases. The teeth alignment, as predicted by our approach, exhibits a high degree of consistency with the actual post-orthodontic results in frontal images. This comprehensive qualitative and quantitative evaluation indicates the practicality and effectiveness of our framework in orthodontics and facial beautification.