Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Objectives The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. Methods PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication. Results The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I-2 = 98.13%, tau(2) = 1.018, p-value < 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012). Conclusion Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done

Three dimensional study to quantify the relationship between facial hard and soft tissue movement as a result of orthognathic surgery

Introduction Prediction of soft tissue changes following orthognathic surgery has been frequently attempted in the past decades. It has gradually progressed from the classic “cut and paste” of photographs to the computer assisted 2D surgical prediction planning; and finally, comprehensive 3D surgical planning was introduced to help surgeons and patients to decide on the magnitude and direction of surgical movements as well as the type of surgery to be considered for the correction of facial dysmorphology. A wealth of experience was gained and numerous published literature is available which has augmented the knowledge of facial soft tissue behaviour and helped to improve the ability to closely simulate facial changes following orthognathic surgery. This was particularly noticed following the introduction of the three dimensional imaging into the medical research and clinical applications. Several approaches have been considered to mathematically predict soft tissue changes in three dimensions, following orthognathic surgery. The most common are the Finite element model and Mass tensor Model. These were developed into software packages which are currently used in clinical practice. In general, these methods produce an acceptable level of prediction accuracy of soft tissue changes following orthognathic surgery. Studies, however, have shown a limited prediction accuracy at specific regions of the face, in particular the areas around the lips. Aims The aim of this project is to conduct a comprehensive assessment of hard and soft tissue changes following orthognathic surgery and introduce a new method for prediction of facial soft tissue changes.   Methodology The study was carried out on the pre- and post-operative CBCT images of 100 patients who received their orthognathic surgery treatment at Glasgow dental hospital and school, Glasgow, UK. Three groups of patients were included in the analysis; patients who underwent Le Fort I maxillary advancement surgery; bilateral sagittal split mandibular advancement surgery or bimaxillary advancement surgery. A generic facial mesh was used to standardise the information obtained from individual patient’s facial image and Principal component analysis (PCA) was applied to interpolate the correlations between the skeletal surgical displacement and the resultant soft tissue changes. The identified relationship between hard tissue and soft tissue was then applied on a new set of preoperative 3D facial images and the predicted results were compared to the actual surgical changes measured from their post-operative 3D facial images. A set of validation studies was conducted. To include: • Comparison between voxel based registration and surface registration to analyse changes following orthognathic surgery. The results showed there was no statistically significant difference between the two methods. Voxel based registration, however, showed more reliability as it preserved the link between the soft tissue and skeletal structures of the face during the image registration process. Accordingly, voxel based registration was the method of choice for superimposition of the pre- and post-operative images. The result of this study was published in a refereed journal. • Direct DICOM slice landmarking; a novel technique to quantify the direction and magnitude of skeletal surgical movements. This method represents a new approach to quantify maxillary and mandibular surgical displacement in three dimensions. The technique includes measuring the distance of corresponding landmarks digitized directly on DICOM image slices in relation to three dimensional reference planes. The accuracy of the measurements was assessed against a set of “gold standard” measurements extracted from simulated model surgery. The results confirmed the accuracy of the method within 0.34mm. Therefore, the method was applied in this study. The results of this validation were published in a peer refereed journal. • The use of a generic mesh to assess soft tissue changes using stereophotogrammetry. The generic facial mesh played a major role in the soft tissue dense correspondence analysis. The conformed generic mesh represented the geometrical information of the individual’s facial mesh on which it was conformed (elastically deformed). Therefore, the accuracy of generic mesh conformation is essential to guarantee an accurate replica of the individual facial characteristics. The results showed an acceptable overall mean error of the conformation of generic mesh 1 mm. The results of this study were accepted for publication in peer refereed scientific journal. Skeletal tissue analysis was performed using the validated “Direct DICOM slices landmarking method” while soft tissue analysis was performed using Dense correspondence analysis. The analysis of soft tissue was novel and produced a comprehensive description of facial changes in response to orthognathic surgery. The results were accepted for publication in a refereed scientific Journal. The main soft tissue changes associated with Le Fort I were advancement at the midface region combined with widening of the paranasal, upper lip and nostrils. Minor changes were noticed at the tip of the nose and oral commissures. The main soft tissue changes associated with mandibular advancement surgery were advancement and downward displacement of the chin and lower lip regions, limited widening of the lower lip and slight reversion of the lower lip vermilion combined with minimal backward displacement of the upper lip were recorded. Minimal changes were observed on the oral commissures. The main soft tissue changes associated with bimaxillary advancement surgery were generalized advancement of the middle and lower thirds of the face combined with widening of the paranasal, upper lip and nostrils regions. In Le Fort I cases, the correlation between the changes of the facial soft tissue and the skeletal surgical movements was assessed using PCA. A statistical method known as ’Leave one out cross validation’ was applied on the 30 cases which had Le Fort I osteotomy surgical procedure to effectively utilize the data for the prediction algorithm. The prediction accuracy of soft tissue changes showed a mean error ranging between (0.0006mm±0.582) at the nose region to (-0.0316mm±2.1996) at the various facial regions

Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed

Characterization of normal facial features and their association with genes

ABSTRACT Background: Craniofacial morphology has been reported to be highly heritable, but little is known about which genetic variants influence normal facial variation in the general population. Aim: To identify facial variation and explore phenotype-genotype associations in a 15-year-old population (2514 females and 2233 males). Subjects and Methods: The subjects involved in this study were recruited from the Avon Longitudinal Study of Parents and Children (ALSPAC). Three-dimensional (3D) facial images were obtained for each subject using two high-resolution Konica Minolta laser scanners. Twenty-one reproducible facial soft tissue landmarks and one constructed mid-endocanthion point (men) were identified and their coordinates were recorded. The 3D facial images were registered using Procrustes analysis (with and without scaling). Principal Component Analysis (PCA) was then employed to identify independent groups ‘principal components, PCs’ of correlated landmark coordinates that represent key facial features contributing to normal facial variation. A novel surface-based method of facial averaging was employed to visualize facial variation. Facial parameters (distances, angles, and ratios) were also generated using facial landmarks. Sex prediction based on facial parameters was explored using discriminant function analysis. A discovery-phase genome-wide association analysis (GWAS) was carried out for 2,185 ALSPAC subjects and replication was undertaken in a further 1,622 ALSPAC individuals. Results: 14 (unscaled) and 17 (scaled) PCs were identified explaining 82% of the total variance in facial form and shape. 250 facial parameters were derived (90 distances, 118 angles, 42 ratios). 24 facial parameters were found to provide sex prediction efficiency of over 70%, 23 of these parameters are distances that describe variation in face height, nose width, and prominence of various facial structures. 54 distances associated with previous reported high heritability and the 14 (unscaled) PCs were included in the discovery-phase GWAS. Four genetic associations with the distances were identified in the discovery analysis, and one of these, the association between the common ‘intronic’ SNP (rs7559271) in PAX3 gene on chromosome (2) and the nasion to mid-endocanthion 3D distance (n-men) was replicated strongly (p = 4 x 10-7). PAX3 gene encodes a transcription factor that plays crucial role in fetal development including craniofacial bones. PAX3 contains two DNA-binding domains, a paired-box domain and a homeodomain. The protein made from PAX3 gene directs the activity of other genes that signal neural crest cells to form specialized tissues such as craniofacial bones. PAX3 different mutations may lead to non-functional PAX3 polypeptides and destroy the ability of the PAX3 proteins to bind to DNA and regulate the activity of other genes to form bones and other specific tissues. Conclusions: The variation in facial form and shape can be accurately quantified and visualized as a multidimensional statistical continuum with respect to the principal components. The derived PCs may be useful to identify and classify faces according to a scale of normality. A strong genetic association was identified between the common SNP (rs7559271) in PAX3 gene on chromosome (2) and the nasion to mid-endocanthion 3D distance (n-men). Variation in this distance leads to nasal bridge prominence

Augmented Reality and Semi-Automated Landmarking of Cephalometric Radiographs

International audienceIn this paper, we propose computer assisted visualization for manual landmarking of specific points on cephalometric radiographs. The signal to noise ratio of radiographs is very low, because of superimposing of anatomic structures, dissymetries or artefacts. On radiographs of children, the localization of cephalometric points presents a great inter-subject, and inter- and intra-expert varibility, which is considerably reduced by considering an adaptative coordinates space. This coordinates space allows us to obtain statistical landmarking of cephalometric points used to define regions of interest. Each region takes advantage of a specific image processing, to enhance local and particular features (bone or suture). An augmented reality image is presented to the human expert, to focus on main sutures and bones in a small region of interest. This method is applied to the nettlesome problem of the interpretation of cephalometric radiographs, and provides satisfying results according to a cephalometric expert