Comparison Study of Extraction Accuracy of 3D Facial Anatomical Landmarks Based on Non-Rigid Registration of Face Template

Wen, A N; Zhu, Yujia; Gao, Z X; Zhang, Yun; Wang, Yong; Wang, Shengjin; Zhao, Yijiao

doi:10.3390/diagnostics13061086

Cited by 5 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we processed the unstructured facial data obtained from the above 3D face scanning to construct a structured 3D facial dataset. For this purpose, we employed a structured 3D face template and the Meshmonk non-rigid registration algorithm [ 22 , 23 , 24 ]. By non-rigidly registering the structured 3D face template to the unstructured patient facial data, we constructed the structured patient facial data with the same topological structure as the structured 3D face template, as illustrated in Figure 2 e. The structured facial template used in this study was the average facial data constructed by our research team based on 30 normal 3D facial data in the previous study, comprising 9856 points and 19,534 triangular meshes [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…By non-rigidly registering the structured 3D face template to the unstructured patient facial data, we constructed the structured patient facial data with the same topological structure as the structured 3D face template, as illustrated in Figure 2e. The structured facial template used in this study was the average facial data constructed by our research team based on 30 normal 3D facial data in the previous study, comprising 9856 points and 19,534 triangular meshes [24]. Based on the above methods, we constructed a structured 3D facial dataset of 400 cases.…”

Section: Constructing a Structured 3d Facial Dataset For Model Traini...mentioning

confidence: 99%

See 1 more Smart Citation

Three-Dimensional Virtual Reconstruction of External Nasal Defects Based on Facial Mesh Generation Network

Qin,

Li,

Wen

et al. 2024

Diagnostics

Self Cite

View full text Add to dashboard Cite

(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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Constructing a Structured 3d Facial Dataset For Model Traini...mentioning

confidence: 99%

Three-Dimensional Virtual Reconstruction of External Nasal Defects Based on Facial Mesh Generation Network

Qin,

Li,

Wen

et al. 2024

Diagnostics

Self Cite

View full text Add to dashboard Cite

show abstract

A Critical Comparison Between Template-Based and Architecture-Reused Deep Learning Methods for Generic 3D Landmarking of Anatomical Structures

Heredia-Lidón,

García-Mascarell,

Echeverry-Quiceno

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Automatic Landmark Annotation and Measurement of 3D Mandibular Morphology Using Non-Rigid Registration: A Preliminary Exploration and Accuracy Assessment

Chen,

Lei,

et al. 2024

The Cleft Palate Craniofacial Journal

View full text Add to dashboard Cite

This study aimed to develop an automatic methodology for mandibular landmarking and measurement using non-rigid registration as well as analyze the accuracy of automatic landmarking and measurements. Statistical analysis. Digital technology center, tertiary hospital. 130 healthy Chinese adults with equal gender distribution, average age 28.2 ± 5.6 years. Four mean shape mesh templates were generated from 100 head CT scans. Following manual indication of landmarks, these templates were applied for automatic landmark annotation and measurements on mandibles from another 30 head CT scans, using non-rigid iterative closest point registration. Differences of landmark coordinates and measurements between automatic and manual annotation were analyzed using mean difference, centroid size, Euclidean distances and intraclass correlation coefficient (ICC), assessing the accuracy and validity of automatic landmark annotation. The majority of automatic landmarks (16/22) did not exhibit consistent displacement to specific direction. ICCs of all landmark coordinates exceed 0.950, with 87.9% larger than 0.990. The average Euclidean distance between manual and automatic landmarks was 2.038 ± 0.947 mm. Most ICCs of linear and angular measurements between manual and automatic annotation (20/26) exceeded 0.900, with the average errors being 1.425 ± 0.973 mm and 2.257 ± 0.649 °, respectively. A novel and efficient method for automatic landmark annotation was established based on non-rigid registration. Its credibility and accuracy in mandibular annotation and measurements were demonstrated.

show abstract

Comparison Study of Extraction Accuracy of 3D Facial Anatomical Landmarks Based on Non-Rigid Registration of Face Template

Cited by 5 publications

References 32 publications

Three-Dimensional Virtual Reconstruction of External Nasal Defects Based on Facial Mesh Generation Network

Three-Dimensional Virtual Reconstruction of External Nasal Defects Based on Facial Mesh Generation Network

A Critical Comparison Between Template-Based and Architecture-Reused Deep Learning Methods for Generic 3D Landmarking of Anatomical Structures

Automatic Landmark Annotation and Measurement of 3D Mandibular Morphology Using Non-Rigid Registration: A Preliminary Exploration and Accuracy Assessment

Contact Info

Product

Resources

About