[Deep learning-assisted construction of three-dimensional face midsagittal plane based on point clouds].

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 (R2) 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.

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

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.

[Preliminary study on three-dimensional morphological reconstruction method for external nose defect based on three-dimensional face template].

Objective: To provide a new solution for the digital design of nasal prostheses, this study explores the three-dimensional (3D) facial morphology completion method for external nasal defects based on the non-rigid registration process of 3D face template. Methods: A total of 20 male patients with tooth defect and dentition defect who visited the Department of Prosthodontics, Peking University School and Hospital of Stomatology from June to December 2022 were selected, age 18-45 years old. The original 3D facial data of patients were collected, and the 3D facial data of the external nose defect was constructed in Geomagic Wrap 2021 software. Using the structured 3D face template data constructed in the previous research of the research group, the 3D face template was deformed and registered to the 3D facial data of external nose defect (based on the morphology of non-defective area) by non-rigid registration algorithm (MeshMonk program), and the personalized deformed data of the 3D face template was obtained, as the complemented facial 3D data. Based on the defect boundary of the 3D facial data of the external nose defect, the complemented external nose 3D data can be cut out from the complemented facial 3D data. Then the nasofacial angle and nasolabial angle of the complemented facial 3D data and the original 3D facial data was compared and analyzed, the ratio between the nose length and mid-face height, nose width and medial canthal distance of the complemented facial 3D data was measured, the edge fit between the edge curve of the complemented external nose 3D data and the defect edge curve of the 3D facial data of external nose defect was evaluated, and the morphological difference of the nose between the complemented external nose 3D data and the original 3D facial data was analyzed. Results: There was no significant statistically difference (t=-0.23, P=0.823; Z=-1.72, P=0.086) in the nasofacial angle (28.2°±2.9°, 28.4°±3.5° respectively) and nasolabial angle [95.4°(19.2°), 99.9°(9.5°) respectively] between the 20 original 3D facial data and the complemented facial 3D data. The value of the ratio of nose length to mid-face height in the complemented facial 3D data was 0.63±0.03, and the value of the ratio of nose width to medial canthal distance was 1.07±0.08. The curve deviation (root mean square value) between the edge curve of the complemented external nose 3D data and the defect edge curve of the 3D facial data of external nose defect was (0.37±0.09) mm, the maximum deviation was (1.14±0.32) mm, and the proportion of the curve deviation value within±1 mm was (97±3)%. The distance of corresponding nose landmarks between the complemented facial 3D data and the original 3D facial data were respectively, Nasion: [1.52(1.92)] mm; Pronasale: (3.27±1.21) mm; Subnasale: (1.99±1.09) mm; Right Alare: (2.64±1.34) mm; Left Alare: (2.42± 1.38) mm. Conclusions: The method of 3D facial morphology completion of external nose defect proposed in this study has good feasibility. The constructed complemented external nose 3D data has good facial coordination and edge fit, and the morphology is close to the nose morphology of the original 3D facial data.

[Automatic determination of mandibular landmarks based on three-dimensional mandibular average model].

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.