Three-Dimensional Semantic Segmentation of Palatal Rugae and Maxillary Teeth and Motion Evaluation of Orthodontically Treated Teeth Using Convolutional Neural Networks.

The segmentation of individual teeth in three-dimensional (3D) dental models is a key step in orthodontic computer-aided design systems. Traditional methods lack robustness when handling challenging cases such as missing or misaligned teeth. to semantically segment maxillary teeth and palatal rugae in 3D textured scans using Convolutional Neural Networks (CNNs) and assess tooth movement after orthodontic treatment using stable rugae references. Building on the robustness of two-dimensional image semantic segmentation, we developed a method to convert 3D textured palate scans into two-dimensional images for segmentation, then back projected them onto the original 3D meshes. A dataset of 100 textured scans from 100 patients seeking orthodontic treatment was manually segmented by orthodontic experts. The proposed 3D segmentation method was applied to these scans. Finally, each pair of segmented 3D scans from the same patient, before and after treatment, was aligned by superimposing them on the stable rugae region. The 3D segmentation method achieved an accuracy of 98.69% and an average Intersection over Union (IoU) of 84.5%. The common stable coordinate frame for both scans using the rugae area as a stable reference enabled the computation of the 3D translational and rotational motions of each maxillary tooth. Neither pre- nor post-processing of the data was required to enhance segmentation. The proposed method enabled successful motion measurement of teeth using the rugal area as a stable reference and providing rotation and translational measurements of the maxillary teeth.