Objective: To explore an automatic landmarking method for anatomical landmarks in the three-dimensional (3D) data of the maxillary complex and preliminarily evaluate its reproducibility and accuracy. Methods: From June 2021 to December 2022, spiral CT data of 31 patients with relatively normal craniofacial morphology were selected from those who visited the Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology. The sample included 15 males and 16 females, with the age of (33.3±8.3) years. The maxillary complex was reconstructed in 3D using Mimics software, and the resulting 3D data of the maxillary complex was mesh-refined using Geomagic software. Two attending physicians and one associate chief physician manually landmarked the 31 maxillary complex datasets, determining 24 anatomical landmarks. The average values of the three expert landmarking results were used as the expert-defined landmarks. One case that conformed to the average 3D morphological characteristics of healthy individuals' craniofacial bones was selected as the template data, while the remaining 30 cases were used as target data. The open-source MeshMonk program (a non-rigid registration algorithm) was used to perform an initial alignment of the template and target data based on 4 landmarks (nasion, left and right zygomatic arch prominence, and anterior nasal spine). The template data was then deformed to the shape of the target data using a non-rigid registration algorithm, resulting in the deformed template data. Based on the unchanged index property of homonymous landmarks before and after deformation of the template data, the coordinates of each landmark in the deformed template data were automatically retrieved as the automatic landmarking coordinates of the homonymous landmarks in the target data, thus completing the automatic landmarking process. The automatic landmarking process for the 30 target data was repeated three times. The root-mean-square distance (RMSD) of the dense corresponding point pairs (approximately 25 000 pairs) between the deformed template data and the target data was calculated as the deformation error of the non-rigid registration algorithm, and the intra-class correlation coefficient (ICC) of the deformation error in the three repetitions was analyzed. The linear distances between the automatic landmarking results and the expert-defined landmarks for the 24 anatomical landmarks were calculated as the automatic landmarking errors, and the ICC values of the 3D coordinates in the three automatic landmarking repetitions were analyzed. Results: The average three-dimensional deviation (RMSD) between the deformed template data and the corresponding target data for the 30 cases was (0.70±0.09) mm, with an ICC value of 1.00 for the deformation error in the three repetitions of the non-rigid registration algorithm. The average automatic landmarking error for the 24 anatomical landmarks was (1.86±0.30) mm, with the smallest error at the anterior nasal spine (0.65±0.24) mm and the largest error at the left oribital (3.27±2.28) mm. The ICC values for the 3D coordinates in the three automatic landmarking repetitions were all 1.00. Conclusions: This study established an automatic landmarking method for three-dimensional data of the maxillary complex based on a non-rigid registration algorithm. The accuracy and repeatability of this method for landmarking normal maxillary complex 3D data were relatively good.
目的： 探索上颌骨复合体三维数据解剖标志点的自动定点方法，并初步评价其可重复性与准确性。 方法： 从2021年6月至2022年12月就诊于北京大学口腔医学院·口腔医院口腔颌面外科的口腔疾病患者螺旋CT资料中，选取31例颅颌面骨骼形态大致正常者的螺旋CT资料，其中男性15例，女性16例，年龄（33.3±8.3）岁，通过Mimics软件对上颌骨复合体进行三维重建，通过Geomagic软件对上颌骨复合体三维数据进行网格优化。由2名主治医师和1名副主任医师对31例上颌骨复合体数据进行人工定点，确定24个解剖标志点，取3人定点均值作为专家定点结果。选择其中1例符合健康人颅颌面骨骼三维形态平均特征的上颌骨复合体数据作为模板数据，其余30例作为目标数据。采用MeshMonk开源程序（一种非刚性配准算法）将模板数据与目标数据基于4个标志点（鼻根点、左右颧弓最突点、前鼻棘点）进行初对齐，再将模板数据基于非刚性配准算法变形为目标数据的形状，得到变形后模板数据，基于模板数据变形前后同名标志点的索引不变特性，自动检索变形后模板数据各标志点坐标，以此作为目标数据同名标志点自动定点坐标，也即完成自动定点过程。30例目标数据的自动定点过程重复3次。计算变形后模板数据与目标数据的稠密对应点对（约25 000对）的三维偏差［均方根距离（root-mean-square distance，RMSD）］，作为非刚性配准算法的变形误差，并分析非刚性配准算法3次变形误差的组内相关系数（intra-class correlation coefficient，ICC）；计算24个解剖标志点自动定点结果与专家定点结果的直线距离作为自动定点误差，并分析3次自动定点三维坐标的ICC值。 结果： 30例变形后模板数据与对应目标数据的三维偏差（RMSD）为（0.70±0.09）mm，非刚性配准算法3次变形误差的ICC值为1.00。24个解剖标志点自动定点误差为（1.86±0.30）mm，前鼻棘点自动定点误差最小，为（0.65±0.24）mm；左眶下缘点自动定点误差最大，为（3.27±2.28）mm；3次自动定点三维坐标ICC值均为1.00。 结论： 本项研究建立了一种基于非刚性配准算法的上颌骨复合体三维数据自动定点方法，该方法应用于正常形态上颌骨复合体三维数据定点的准确性与可重复性较好。.