目的: 探索一种高效、自动确定三维下颌骨数据解剖标志点的方法，并对该方法的定点效果进行初步评价。方法: 选取40例颅颌面三维形态正常患者的CT数据(其中30例用来建立三维下颌骨平均模型，10例作为测试本研究方法确定下颌骨标志点效果的测试数据)，将数据导入到Mimics软件中进行下颌骨三维重建。在这40例三维重建后的下颌骨数据中，选取与中国人下颌骨特征均值更为接近的30例，在MATLAB软件中基于普氏分析(Procrustes analysis)算法对此30例下颌骨数据进行尺寸归一化处理，并在Geomagic Wrap软件中，构建上述30例下颌骨数据的三维平均形状模型，通过对称化处理、曲率采样、索引标记等过程，构建出具有18 996个类标志点和19个下颌骨解剖标志点索引的三维下颌骨结构化模板。应用开源非刚性配准算法程序Meshmonk，将上述构建的三维下颌骨模板通过非刚性变形与患者三维下颌骨数据进行匹配，获得患者三维下颌骨数据的19个解剖标志点位置。与口腔专家手动标注的标志点位置误差(定点误差)进行比较，评价本方法的准确性。结果: 将本研究方法应用于10例无显著下颌骨形态畸形患者数据，19个标志点的平均定点误差为1.42 mm，其中最小和最大误差分别为喙突顶点[右：(1.01±0.44) mm；左：(0.56±0.14) mm]和下颌升支前缘点[右：(2.52±0.95) mm；左：(2.57±1.10) mm]，中线点平均定点误差为(1.15±0.60) mm，双侧点平均定点误差为(1.51±0.67) mm。结论: 基于三维下颌骨平均模型和非刚性配准算法的三维下颌骨解剖标志点自动确定方法，可有效提高三维下颌骨数据特征自动标注的效率，其对无显著畸形下颌骨数据解剖标志点的自动确定效果可基本满足口腔临床应用的需求，对畸形下颌骨数据的标注效果有待进一步测试。

Objective: To explore an efficient and automatic method for determining the anatomical landmarks of three-dimensional(3D) mandibular data, and to preliminarily evaluate the performance of the method. Methods: The CT data of 40 patients with normal craniofacial morphology were collected (among them, 30 cases were used to establish the 3D mandibular average model, and 10 cases were used as test datasets to validate the performance of this method in determining the mandibular landmarks), and the 3D mandibular data were reconstructed in Mimics software. Among the 40 cases of mandibular data after the 3D reconstruction, 30 cases that were more similar to the mean value of Chinese mandibular features were selected, and the size of the mandibular data of 30 cases was normalized based on the Procrustes analysis algorithm in MATLAB software. Then, in the Geomagic Wrap software, the 3D mandibular average shape model of the above 30 mandibular data was constructed. Through symmetry processing, curvature sampling, index marking and other processing procedures, a 3D mandible structured template with 18 996 semi-landmarks and 19 indexed mandibular anatomical landmarks were constructed. The open source non-rigid registration algorithm program Meshmonk was used to match the 3D mandible template constructed above with the tested patient's 3D mandible data through non-rigid deformation, and 19 anatomical landmark positions of the patient's 3D mandible data were obtained. The accuracy of the research method was evaluated by comparing the distance error of the landmarks manually marked by stomatological experts with the landmarks marked by the method of this research. Results: The method of this study was applied to the data of 10 patients with normal mandibular morphology. The average distance error of 19 landmarks was 1.42 mm, of which the minimum errors were the apex of the coracoid process [right: (1.01±0.44) mm; left: (0.56±0.14) mm] and maximum errors were the anterior edge of the lowest point of anterior ramus [right: (2.52±0.95) mm; left: (2.57±1.10) mm], the average distance error of the midline landmarks was (1.15±0.60) mm, and the average distance error of the bilateral landmarks was (1.51±0.67) mm. Conclusion: The automatic determination method of 3D mandibular anatomical landmarks based on 3D mandibular average shape model and non-rigid registration algorithm established in this study can effectively improve the efficiency of automatic labeling of 3D mandibular data features. The automatic determination of anatomical landmarks can basically meet the needs of oral clinical applications, and the labeling effect of deformed mandible data needs to be further tested.