Clinically oriented automatic three-dimensional enamel segmentation via deep learning.

Abstract

Background: Establishing accurate, reliable, and convenient methods for enamel segmentation and analysis is crucial for effectively planning endodontic, orthodontic, and restorative treatments, as well as exploring the evolutionary patterns of mammals. However, no mature, non-destructive method currently exists in clinical dentistry to quickly, accurately, and comprehensively assess the integrity and thickness of enamel chair-side. This study aims to develop a deep learning work, 2.5D Attention U-Net, trained on small sample datasets, for the automatical, efficient, and accurate segmentation of enamel across all teeth in clinical settings.

Methods: We propose a fully automated computer-aided enamel segmentation model based on an instance segmentation network, 2.5D Attention U-Net. After data annotation and augmentation, the model is trained using manually annotated segmented enamel data, and its performance is evaluated using the Dice similarity coefficient metrics. A satisfactory image segmentation model is applied to generate a 3D enamel model for each tooth and to calculate the thickness value of individual enclosed 3D enamel meshes using a normal ray-tracing directional method.

Results: The model achieves the Dice score on the enamel segmentation task of 96.6%. This study provides an intuitive visualization of irregular enamel morphology and a quantitative analysis of three-dimensional enamel thickness variations. The results indicate that enamel is thickest at the incisal edges of anterior teeth and the cusps of posterior teeth, thinning towards the roots. For posterior teeth, the enamel is thinnest at the central fossae area, with mandibular molars having thicker enamel in the central fossae compared to maxillary molars. The average enamel thickness of maxillary incisors, canines, and premolars is greater than that of mandibular incisors, while the opposite is true for molars. Although there are individual variations in enamel thickness, the average enamel thickness graduallly increases from the incisors to the molars among all teeth within the same quadrant.

Conclusions: This study introduces an automatic, efficient, and accurate 2.5D Attention U-Net system to enhance precise and efficient chair-side diagnosis and treatment of enamel-related diseases in clinical settings, marking a significant advancement in automated diagnostics for enamel-related conditions.