基于非刚性配准算法的上颌骨复合体三维数据自动定点研究  

Study on the method of automatically determining maxillary complex landmarks based on non-rigid registration algorithms

作　　者：高梓翔 王晶[2] 温奥楠 朱玉佳 秦庆钊 王勇[1] 赵一姣[1] Gao Zixiang;Wang Jing;Wen Aonan;Zhu Yujia;Qin Qingzhao;Wang Yong;Zhao Yijiao(Center of Digital Dentistry,Faculty of Prosthodontics,Peking University School and Hospital of Stomatology&National Center for 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;Department of Oral and Maxillofacial Surgery,Peking University School and Hospital of Stomatology&National Center for Stomatology&National Clinical Research Center for Oral Diseases&National Engineering Research Center of Oral Biomaterials and Digital Medical Devices&Beijing Key Laboratory of Digital Stomatology,Beijing 100081,China)

机构地区：[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 [2]北京大学口腔医学院·口腔医院口腔颌面外科、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、口腔数字医学北京市重点实验室,北京、100081

出　　处：《中华口腔医学杂志》2023年第6期554-560,共7页Chinese Journal of Stomatology

基　　金：国家重点研发计划(2022YFC2405401);国家自然科学基金(82071171,82271039);甘肃省重点研发计划(21YF5FA165);北大医学顶尖学科及学科群发展专项(BMU2022XKQ003)。

摘　　要：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25000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�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 25000 pai

关 键 词：人工智能 上颌骨 测颅法 成像 三维 解剖标志点 非刚性配准 

分 类 号：R782[医药卫生—口腔医学]