[Deep learning-assisted construction of three-dimensional face midsagittal plane based on point clouds].

Objective: To establish an intelligent registration algorithm under the framework of original-mirror alignment algorithm to construct three-dimensional(3D) facial midsagittal plane automatically. Dynamic Graph Registration Network (DGRNet) was established to realize the intelligent registration, in order to provide a reference for clinical digital design and analysis. Methods: Two hundred clinical patients without significant facial deformities were collected from October 2020 to October 2022 at Peking University School and Hospital of Stomatology. The DGRNet consists of constructing the feature vectors of key points in point original and mirror point clouds (X, Y), obtaining the correspondence of key points, and calculating the rotation and translation by singular value decomposition. Original and mirror point clouds were registrated and united. The principal component analysis (PCA) algorithm was used to obtain the DGRNet alignment midsagittal plane. The model was evaluated based on the coefficient of determination (R2) index for the translation and rotation matrix of test set. The angle error was evaluated on the 3D facial midsagittal plane constructed by the DGRNet alignment midsagittal plane and the iterative closet point(ICP) alignment midsagittal plane for 50 cases of clinical facial data. Results: The average angle error of the DGRNet alignment midsagittal plane and ICP alignment midsagittal plane was 1.05°±0.56°, and the minimum angle error was only 0.13°. The successful detection rate was 78%(39/50) within 1.50° and 90% (45/50)within 2.00°. Conclusions: This study proposes a new solution for the construction of 3D facial midsagittal plane based on the DGRNet alignment method with intelligent registration, which can improve the efficiency and effectiveness of treatment to some extent.目的： 基于可变图结构配准网络（dynamic graph registration network，DGRNet）模型，建立一种可实现三维点云智能配准的本体-镜像关联深度学习算法，以实现三维颜面正中矢状面的自动化构建，为口腔临床数字化设计与分析提供参考。 方法： 收集2020年10月至2022年10月就诊于北京大学口腔医学院·口腔医院修复科、正颌外科和口腔正畸科的无明显颜面畸形的牙体缺损或缺失或错（牙合）畸形患者200例。通过数据增强（平移和旋转）的方式获得1 200例三维颜面数据，分为训练集（800例）、验证集（200例）、测试集（200例），用于DGRNet模型训练与测试。DGRNet模型包含构造本体与镜像点云中关键点的特征向量、基于特征向量获取本体和镜像点云中关键点的对应关系，并通过奇异值分解计算旋转矩阵和平移矩阵。基于DGRNet模型实现本体点云与镜像点云的智能配准，获得本体-镜像联合点云，并采用主成分分析算法获得DGRNet模型正中矢状面。基于决定系数（coefficient of determination，R2）指标对测试集平移及旋转矩阵进行模型评价，以迭代最近点（iterative closest point，ICP）算法构建的三维颜面正中矢状面作为真值。选择上述200例临床患者中的50例数据，对DGRNet模型与ICP算法构建的三维颜面正中矢状面进行角度误差评价。 结果： 基于200例三维颜面数据测试DGRNet模型旋转矩阵R2为0.90，平移矩阵R2为0.94。构建50例三维颜面数据正中矢状面共用时3 s，DGRNet模型与ICP算法构建的三维颜面正中矢状面角度误差为1.05°±0.56°，最小误差为0.13°，1.50°以内的准确率为39/50（78%），2.00°以内的准确率为45/50（90%）。 结论： 本项研究提出的基于三维点云智能配准的DGRNet模型可构建三维颜面正中矢状面，并在一定程度上提升诊疗效率和效果。.