Self-equilibrium segmentation of near-infrared images of dental microcracks

Abstract

The issue of missing teeth caused by dental microcracks has become a central topic in dental clinical medicine. Near-infrared technology combined with advanced image segmentation algorithms has become one of the leading technical routes for dental clinical medicine diagnosis. In this study, the NIR image segmentation method of dental microcracks was studied in depth to further utilize the advantages of near-infrared imaging technology in the detection technology of dental microcracks. The main contributions and research contents of this paper include (1) we designed a near-infrared imaging system for dental imaging, which provides solid data support for the study of advanced segmentation algorithms; (2) we designed a self-equilibrium segmentation algorithm for occult fracture of teeth based on U-Net++. The core module of this algorithm includes a multi-resolution residual encoder and a self-equilibrium composite loss function. The multi-scale residual encoder significantly improves the feature extraction capability of U-Net++. The self-equilibrium combined loss function ensures the consistency of the model’s attention to positive and negative samples, which enhances the robustness of U-Net++. (3) We have demonstrated through extensive experiments that our algorithm performs better than other dental occultation segmentation methods. This study is valuable for improving the diagnostic accuracy of dental occult fractures and promptly detecting the potential danger of the dental.