(1) Background: Three-dimensional (3D) facial anatomical landmarks are the premise and foundation of facial morphology analysis. At present, there is no ideal automatic determination method for 3D facial anatomical landmarks. This research aims to realize the automatic determination of 3D facial anatomical landmarks based on the non-rigid registration algorithm developed by our research team and to evaluate its landmark localization accuracy. (2) Methods: A 3D facial scanner, Face Scan, was used to collect 3D facial data of 20 adult males without significant facial deformities. Using the radial basis function optimized non-rigid registration algorithm, TH-OCR, developed by our research team (experimental group: TH group) and the non-rigid registration algorithm, MeshMonk (control group: MM group), a 3D face template constructed in our previous research was deformed and registered to each participant’s data. The automatic determination of 3D facial anatomical landmarks was realized according to the index of 32 facial anatomical landmarks determined on the 3D face template. Considering these 32 facial anatomical landmarks manually selected by experts on the 3D facial data as the gold standard, the distance between the automatically determined and the corresponding manually selected facial anatomical landmarks was calculated as the “landmark localization error” to evaluate the effect and feasibility of the automatic determination method (template method). (3) Results: The mean landmark localization error of all facial anatomical landmarks in the TH and MM groups was 2.34 ± 1.76 mm and 2.16 ± 1.97 mm, respectively. The automatic determination of the anatomical landmarks in the middle face was better than that in the upper and lower face in both groups. Further, the automatic determination of anatomical landmarks in the center of the face was better than in the marginal part. (4) Conclusions: In this study, the automatic determination of 3D facial anatomical landmarks was realized based on non-rigid registration algorithms. There is no significant difference in the automatic landmark localization accuracy between the TH-OCR algorithm and the MeshMonk algorithm, and both can meet the needs of oral clinical applications to a certain extent.

Aim: The purpose of this study was to evaluate the accuracy and efficacy of a new wireframe template methodology in analyzing three-dimensional facial soft tissue asymmetry. Materials and methods: Three-dimensional facial soft tissue data were obtained for 24 patients. The wireframe template was established by identifying 34 facial landmarks and then forming a template on the face with the MeshLab 2020 software. The angle asymmetry index was automatically scored using the template. The mirroring and overlapping technique is accepted as the golden standard method to diagnose facial asymmetry by acquiring deviation values of one’s face. Consistency rates between the two methodologies were determined through a statistical comparison of the angle asymmetry index and deviation values. Results: Overall consistency rates in the labial, mandibular angle, cheek, chin, and articular regions were 87.5%, 95.8%, 87.5%, 91.7%, and 100%, respectively. Regions with consistency rates in three dimensions of more than 85% are the x-axis and the z-axis of all regions and the y-axis of the mandibular angle, chin, and articular region. Conclusions: Soft tissue facial asymmetry can be diagnosed accurately and effectively by using a three-dimensional soft tissue spatial angle wireframe template. Precise localization of asymmetry can be offered, and indiscernible tiny asymmetry can be identified.

(1) Background: In digital-technology-assisted nasal defect reconstruction methods, a crucial step involves utilizing computer-aided design to virtually reconstruct the nasal defect’s complete morphology. However, current digital methods for virtual nasal defect reconstruction have yet to achieve efficient, precise, and personalized outcomes. In this research paper, we propose a novel approach for reconstructing external nasal defects based on the Facial Mesh Generation Network (FMGen-Net), aiming to enhance the levels of automation and personalization in virtual reconstruction. (2) Methods: We collected data from 400 3D scans of faces with normal morphology and combined the structured 3D face template and the Meshmonk non-rigid registration algorithm to construct a structured 3D facial dataset for training FMGen-Net. Guided by defective facial data, the trained FMGen-Net automatically generated an intact 3D face that was similar to the defective face, and maintained a consistent spatial position. This intact 3D face served as the 3D target reference face (3D-TRF) for nasal defect reconstruction. The reconstructed nasal data were extracted from the 3D-TRF based on the defective area using reverse engineering software. The ‘3D surface deviation’ between the reconstructed nose and the original nose was calculated to evaluate the effect of 3D morphological restoration of the nasal defects. (3) Results: In the simulation experiment of 20 cases involving full nasal defect reconstruction, the ‘3D surface deviation’ between the reconstructed nasal data and the original nasal data was 1.45 ± 0.24 mm. The reconstructed nasal data, constructed from the personalized 3D-TRF, accurately reconstructed the anatomical morphology of nasal defects. (4) Conclusions: This paper proposes a novel method for the virtual reconstruction of external nasal defects based on the FMGen-Net model, achieving the automated and personalized construction of the 3D-TRF and preliminarily demonstrating promising clinical application potential.

Purpose: Research on primary maxillary central incisors (PMCIs) remains limited, and their morphological variations have rarely been documented. This study aimed to reconstruct anatomy of PMCIs in children from Beijing (China) and to analyse their commonalities and variations. Methods: Employing a threshold-based semi-automated region segmentation method, anatomical models of 101 PMCIs were reconstructed from existing cone-beam computed tomography (CBCT) images. Models were classified according to the Vertucci classification. For each variant type, representative morphological parameters of the hard tissue, pulp chamber, and canals were measured. The type with the highest prevalence was selected as the main type and its average model was constructed, representing the most common characteristics of PMCIs. Results: All PMCIs had a single root, whereas novel canal variations were identified. The most common canal type was Type I (61.4%), followed by Type V (20.8%) and Type III (17.8%). Anatomical parameters of main-type PMCIs were as follows: hard-tissue length = 15.76 ± 0.89 mm; pulp chamber and canal length = 12.94 ± 1.15 mm; and apical labial curvature angle was 22.57°. Statistical analysis indicated no differences between left and right, and no sex-related differences (p > 0.05). Statistically significant differences between the main-type and other variants were noted for several pulp-chamber and canal measurements (p < 0.05), but not for hard-tissue measurements (p > 0.05). An average main-type model was constructed; its inner and outer profiles conformed to the general characteristics of main-type PMCIs. Conclusion: PMCI canal variations were more complex than previously recognised, with uniform hard-tissue anatomy. Furthermore, an average main-type model was constructed, as a potentially valuable tool for dental education.

Background: Despite previous anatomical studies on PMFMs, detailed classification and comprehensive measurements remain limited, and the association between crown and root morphology has not been systematically investigated. This study aims to reconstruct three-dimensional anatomical models of PMFMs from intact CBCT images obtained from children in Beijing, China, to characterize variations in crown and root morphology and their relationships, and to conduct detailed quantitative and statistical analyses based on refined classifications. Methods: Three-dimensional (3D) anatomical models of 67 intact PMFMs were reconstructed from CBCT scans using a semi-automatic threshold-based region segmentation method. Key morphological parameters of the crown and root were measured. Samples were primarily classified based on the ratio of mesiodistal to buccolingual crown diameter (R) and the number of roots (N), respectively, and their association was explored. A secondary classification was conducted by incorporating the number of root canals (n). For subgroups with sufficient sample sizes, detailed morphological parameters were measured and compared among types. Results: Two crown variants were identified. R > 1 was observed in 59 teeth (88.1%), with a mean value of 1.11 ± 0.06, while R < 1 was observed in 8 teeth (11.9%), with a mean of 0.94 ± 0.03. A significant difference was found between the R values ( p < 0.05). Two root number variants were identified. Fifty-eight teeth (86.6%) had two mesiodistally aligned roots, all with R > 1. Nine teeth (13.4%) had three roots, in which 8 (11.9%) had R < 1; and 1 (1.5%) had R > 1. A significant negative correlation was found between R and N ( r = -0.61, p < 0.05), and ROC curve analysis confirmed that R = 1.00 was the optimal cutoff for distinguishing two-rooted from three-rooted PMFMs (AUC = 1.000). Besides, a secondary classification identified seven types, among which the mesiobuccal canal was consistently the longest ( p < 0.05). Conclusion: PMFMs exhibit considerable morphological variations. The ratio of mesiodistal to buccolingual crown diameter (R) is a useful predictor of root number, and the mesiobuccal canal is significantly longer than the other canals. These features may aid clinicians in preoperative planning.