Deep learning-assisted construction of three-dimensional face midsagittal plane based on point clouds
10.3760/cma.j.cn112144-20230825-00110
- VernacularTitle:基于深度学习点云配准的三维颜面正中矢状面构建算法研究
- Author:
Yujia ZHU
1
;
Zhenguang LIU
;
Aonan WEN
;
Zixiang GAO
;
Qingzhao QIN
;
Xiangling FU
;
Yong WANG
;
Jinpeng CHEN
;
Yijiao ZHAO
Author Information
1. 北京大学口腔医学院·口腔医院口腔医学数字化研究中心 口腔修复教研室 国家口腔医学中心 国家口腔疾病临床医学研究中心 口腔生物材料和数字诊疗装备国家工程研究中心 国家卫生健康委口腔医学计算机应用工程技术研究中心 口腔数字医学北京市重点实验室，北京 100081
- Keywords:
Artificial intelligence;
Oral medicine;
Image processing, computer-assisted;
Face;
Deep learning;
Midsagittal plane
- From:
Chinese Journal of Stomatology
2023;58(11):1178-1183
- CountryChina
- Language:Chinese
-
Abstract:
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 (R 2) 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.
