Wen A N, Zhu Y J, Zheng S W, Xiao N, Gao Z X, Fu X L, Wang Y, Zhao Yijiao
Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China.
Center of Digital Dentistry, Faculty of Prosthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China.
Zhonghua Kou Qiang Yi Xue Za Zhi. 2022 Apr 9;57(4):358-365. doi: 10.3760/cma.j.cn112144-20210913-00409.
To explore the establishment of an efficient and automatic method to determine anatomical landmarks in three-dimensional (3D) facial data, and to evaluate the effectiveness of this method in determining landmarks. A total of 30 male patients with tooth defect or dentition defect (with good facial symmetry) who visited the Department of Prosthodontics, Peking University School and Hospital of Stomatology from June to August 2021 were selected, and these participants' age was between 18-45 years. 3D facial data of patients was collected and the size normalization and overlap alignment were performed based on the Procrustes analysis algorithm. A 3D face average model was built in Geomagic Studio 2013 software, and a 3D face template was built through parametric processing. MeshLab 2020 software was used to determine the serial number information of 32 facial anatomical landmarks (10 midline landmarks and 22 bilateral landmarks). Five male patients with no mandibular deviation and 5 with mild mandibular deviation were selected from the Department of Orthodontics or Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology from June to August 2021. 3D facial data of patients was collected as test data. Based on the 3D face template and the serial number information of the facial anatomical landmarks, the coordinates of 32 facial anatomical landmarks on the test data were automatically determined with the help of the MeshMonk non-rigid registration algorithm program, as the data for the template method to determine the landmarks. The positions of 32 facial anatomical landmarks on the test data were manually determined by the same attending physician, and the coordinates of the landmarks were recorded as the data for determining landmarks by the expert method. Calculated the distance value of the coordinates of facial anatomical landmarks between the template method and the expert method, as the landmark localization error, and evaluated the effect of the template method in determining the landmarks. For 5 patients with no mandibular deviation, the landmark localization error of all facial anatomical landmarks by template method was (1.65±1.19) mm, the landmark localization error of the midline facial anatomical landmarks was (1.19±0.45) mm, the landmark localization error of bilateral facial anatomical landmarks was (1.85±1.33) mm. For 5 patients with mild mandibular deviation, the landmark localization error of all facial anatomical landmarks by template method was (2.55±2.22) mm, the landmark localization error of the midline facial anatomical landmarks was (1.85±1.13) mm, the landmark localization error of bilateral facial anatomical landmarks was (2.87±2.45) mm. The automatic determination method of facial anatomical landmarks proposed in this study has certain feasibility, and the determination effect of midline facial anatomical landmarks is better than that of bilateral facial anatomical landmarks. The effect of determining facial anatomical landmarks in patients without mandibular deviation is better than that in patients with mild mandibular deviation.
探索建立一种高效、自动的方法来确定三维(3D)面部数据中的解剖标志点,并评估该方法在确定标志点方面的有效性。选取2021年6月至8月期间到北京大学口腔医学院口腔修复科就诊的30例牙体缺损或牙列缺损(面部对称性良好)的男性患者,这些参与者的年龄在18 - 45岁之间。收集患者的3D面部数据,并基于普氏分析算法进行尺寸归一化和重叠对齐。在Geomagic Studio 2013软件中构建3D面部平均模型,并通过参数化处理构建3D面部模板。使用MeshLab 2020软件确定32个面部解剖标志点(10个中线标志点和22个双侧标志点)的序列号信息。从北京大学口腔医学院正畸科或口腔颌面外科选取2021年6月至8月期间5例无下颌偏斜的男性患者和5例轻度下颌偏斜的男性患者。收集患者的3D面部数据作为测试数据。基于3D面部模板和面部解剖标志点的序列号信息,借助MeshMonk非刚性配准算法程序自动确定测试数据上32个面部解剖标志点的坐标,作为模板法确定标志点的数据。由同一位主治医师手动确定测试数据上32个面部解剖标志点的位置,并记录标志点的坐标作为专家法确定标志点的数据。计算模板法和专家法之间面部解剖标志点坐标的距离值,作为标志点定位误差,并评估模板法在确定标志点方面的效果。对于5例无下颌偏斜的患者,模板法所有面部解剖标志点的标志点定位误差为(1.65±1.19)mm,中线面部解剖标志点的标志点定位误差为(1.19±0.45)mm,双侧面部解剖标志点的标志点定位误差为(1.85±1.33)mm。对于5例轻度下颌偏斜的患者,模板法所有面部解剖标志点的标志点定位误差为(2.55±2.22)mm,中线面部解剖标志点的标志点定位误差为(1.85±1.13)mm,双侧面部解剖标志点的标志点定位误差为(2.87±2.45)mm。本研究提出的面部解剖标志点自动确定方法具有一定的可行性,中线面部解剖标志点的确定效果优于双侧面部解剖标志点。无下颌偏斜患者面部解剖标志点的确定效果优于轻度下颌偏斜患者。