Wu Juhao, Huang Yuanchang, He Jiayan, Chen Kunjing, Wang Wenlong, Li Xiao
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China.
Department of Stomatology, General Hospital of Southern Theater Command of PLA, Guangzhou, 510010, China.
BMC Oral Health. 2025 Aug 2;25(1):1292. doi: 10.1186/s12903-025-06576-0.
Accurate restoration and reconstruction of tooth morphology are crucial in restorative dentistry, implantology, and forensic odontology. Traditional methods, like manual wax modeling and template-based computer-aided design (CAD), struggle with accuracy, personalization, and efficiency. To address the challenge, we propose an innovative and efficient deep learning-based framework designed for the automatic restoration and reconstruction of tooth morphology.
The proposed method contains three stages. Firstly, an RGB image of a defective tooth is inputted into the restoration network, which fills in the missing regions to produce a complete RGB image of the tooth. The resulting image is then converted to a grayscale image in the preprocessing stage to ensure compatibility with the subsequent reconstruction process. Finally, the 3D reconstruction network utilizes the grayscale image to generate a detailed 3D mesh model of the tooth.
The experimental results demonstrate that the proposed method achieves superior performance in restoration quality, reconstruction accuracy, generalization, and inference speed, with an average time of 12 s per image. Notably, compared to the original Pixel2Mesh, the improved ResNet50-based Pixel2Mesh enhances the average F-Score, CD, and EMD for reconstructed tooth models by 26.5%, 34.7%, and 22.3%, respectively.
The approach proposed in this paper offers a promising solution for personalized intelligent, and efficient tooth restoration and reconstruction, providing a valuable tool for dental diagnostics and treatment planning.
在修复牙科、种植牙学和法医牙科学中，准确恢复和重建牙齿形态至关重要。传统方法，如手工蜡型制作和基于模板的计算机辅助设计（CAD），在准确性、个性化和效率方面存在困难。为应对这一挑战，我们提出了一种创新且高效的基于深度学习的框架，用于牙齿形态的自动恢复和重建。
所提出的方法包含三个阶段。首先，将一颗有缺陷牙齿的RGB图像输入到修复网络中，该网络填充缺失区域以生成牙齿的完整RGB图像。然后，在预处理阶段将所得图像转换为灰度图像，以确保与后续的重建过程兼容。最后，三维重建网络利用灰度图像生成牙齿的详细三维网格模型。
实验结果表明，所提出的方法在恢复质量、重建准确性、泛化能力和推理速度方面具有卓越性能，每张图像平均耗时12秒。值得注意的是，与原始的Pixel2Mesh相比，改进后的基于ResNet50的Pixel2Mesh分别将重建牙齿模型的平均F分数、CD和EMD提高了26.5%、34.7%和22.3%。
本文提出的方法为个性化、智能且高效的牙齿修复和重建提供了一个有前景的解决方案，为牙科诊断和治疗计划提供了一个有价值的工具。
