Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans.

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and face. Cephalometric analysis has been traditionally conducted with the use of 2D radiographs and landmark sets and restricted to size, linear and angular measurements, and 2D relationships. The increasing use of 3D cone beam computed tomography (CBCT) scans in the dental field dictates the need for the evolution to 3D cephalometric analysis, which incorporates shape and a more realistic analysis of longitudinal development in all three planes. This study is a demonstration of 3D cephalometric analysis with the use of a validated set of skeletal tissue landmarks on human CBCT scans. Detailed instructions for the annotation of each landmark on a 3D volume are provided as part of a step-by-step protocol. The generated measurements and 3D coordinates of the landmarks can be exported and used both for clinical and research purposes. The introduction of 3D cephalometric analysis in basic and clinical craniofacial studies will lead to future advancements in the field of craniofacial growth and development.

Relational reasoning network for anatomical landmarking.

Purpose: We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them. Toward this, we propose a simple, yet efficient, deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations among the landmarks in CMF bones; specifically, mandible, maxilla, and nasal bones. Approach: The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units. For a given few landmarks as input, RRN treats the landmarking process similar to a data imputation problem where predicted landmarks are considered missing. Results: We applied RRN to cone-beam computed tomography scans obtained from 250 patients. With a fourfold cross-validation technique, we obtained an average root mean squared error of < 2 mm per landmark. Our proposed RRN has revealed unique relationships among the landmarks that help us in inferring informativeness of the landmark points. The proposed system identifies the missing landmark locations accurately even when severe pathology or deformations are present in the bones. Conclusions: Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for CMF surgeries. Achieving this goal without the need for explicit bone segmentation addresses a major limitation of segmentation-based approaches, where segmentation failure (as often is the case in bones with severe pathology or deformation) could easily lead to incorrect landmarking. To the best of our knowledge, this is the first-of-its-kind algorithm finding anatomical relations of the objects using deep learning.

Loeys-Dietz and Shprintzen-Goldberg syndromes: analysis of TGF-ß-opathies with craniofacial manifestations using an innovative multimodality method.

BACKGROUND: Elevated transforming growth factor-beta (TGF-ß) signalling has been implicated in the pathogenesis of Loeys-Dietz syndrome (LDS) and Shprintzen-Goldberg syndrome (SGS). In this study, we provide a qualitative and quantitative analysis of the craniofacial and functional features among the LDS subtypes and SGS. METHODS: We explore the variability within and across a cohort of 44 patients through deep clinical phenotyping, three-dimensional (3D) facial photo surface analysis, cephalometric and geometric morphometric analyses of cone-beam CT scans. RESULTS: The most common craniofacial features detected in this cohort include mandibular retrognathism (84%), flat midface projection (84%), abnormal eye shape (73%), low-set ears (73%), abnormal nose (66%) and lip shape (64%), hypertelorism (41%) and a relatively high prevalence of nystagmus/strabismus (43%), temporomandibular joint disorders (38%) and obstructive sleep apnoea (23%). 3D cephalometric analysis demonstrated an increased cranial base angle with shortened anterior cranial base and underdevelopment of the maxilla and mandible, with evidence of a reduced pharyngeal airway in 55% of those analysed. Geometric morphometric analysis confirmed that the greatest craniofacial shape variation was among patients with LDS type 2, with distinct clustering of patients with SGS. CONCLUSIONS: This comprehensive phenotypic approach identifies developmental abnormalities that segregate to mutation variants along the TGF-ß signalling pathway, with a particularly severe phenotype associated with TGFBR2 and SKI mutations. Multimodality assessment of craniofacial anomalies objectively reveals the impact of mutations of the TGF-ß pathway with perturbations associated with the cranium and cranial base with severe downstream effects on the orbit, maxilla and mandible with the resultant clinical phenotypes.

Malocclusion Classification on 3D Cone-Beam CT Craniofacial Images Using Multi-Channel Deep Learning Models.

Analyzing and interpreting cone-beam computed tomography (CBCT) images is a complicated and often time-consuming process. In this study, we present two different architectures of multi-channel deep learning (DL) models: "Ensemble" and "Synchronized multi-channel", to automatically identify and classify skeletal malocclusions from 3D CBCT craniofacial images. These multi-channel models combine three individual single-channel base models using a voting scheme and a two-step learning process, respectively, to simultaneously extract and learn a visual representation from three different directional views of 2D images generated from a single 3D CBCT image. We also employ a visualization method called "Class-selective Relevance Mapping" (CRM) to explain the learned behavior of our DL models by localizing and highlighting a discriminative area within an input image. Our multi-channel models achieve significantly better performance overall (accuracy exceeding 93%), compared to single-channel DL models that only take one specific directional view of 2D projected image as an input. In addition, CRM visually demonstrates that a DL model based on the sagittal-left view of 2D images outperforms those based on other directional 2D images.Clinical Relevance- the proposed method aims at assisting orthodontist to determine the best treatment path for the patient be it orthodontic or surgical treatment or a combination of both.

Craniofacial Analysis May Indicate Co-Occurrence of Skeletal Malocclusions and Associated Risks in Development of Cleft Lip and Palate.

Non-syndromic orofacial clefts encompass a range of morphological changes affecting the oral cavity and the craniofacial skeleton, of which the genetic and epigenetic etiologic factors remain largely unknown. The objective of this study is to explore the contribution of underlying dentofacial deformities (also known as skeletal malocclusions) in the craniofacial morphology of non-syndromic cleft lip and palate patients (nsCLP). For that purpose, geometric morphometric analysis was performed using full skull cone beam computed tomography (CBCT) images of patients with nsCLP (n = 30), normocephalic controls (n = 60), as well as to sex- and ethnicity- matched patients with an equivalent dentofacial deformity (n = 30). Our outcome measures were shape differences among the groups quantified via principal component analysis and associated principal component loadings, as well as mean shape differences quantified via a Procrustes distance among groups. According to our results, despite the shape differences among all three groups, the nsCLP group shares many morphological similarities in the maxilla and mandible with the dentofacial deformity group. Therefore, the dentoskeletal phenotype in nsCLP could be the result of the cleft and the coexisting dentofacial deformity and not simply the impact of the cleft.

Development and Validation of Novel Three-Dimensional Craniofacial Landmarks on Cone-Beam Computed Tomography Scans.

As cone-beam computed tomography (CBCT) scans become increasingly common, it is vital to have reliable 3-dimensional (3D) landmarks for quantitative analysis of craniofacial skeletal morphology. While some studies have developed and used 3D landmarks, these landmark sets are generally small and derived primarily from previous 2-dimensional (2D) cephalometric landmarks. These derived landmarks lack information in parts of the skull such as the cranial base, which is an important feature for cranial growth and development. The authors see a real need for development and validation of 3D landmarks, particularly bilateral landmarks, across the skull for improved cephalometric analysis. The primary objective of this study is to develop and validate a set of 61 3D anatomical landmarks on the face, cranial base, mandible, and teeth for use in clinical and research studies involving CBCT imaging. Each landmark was placed 3 times by 3 separate trained observers on a set of 10 anonymized CBCT patient scans. Intra-rater and inter-rater estimates of consistency and agreement were calculated using the intraclass correlation coefficient. Measurement error was calculated per landmark and per X, Y, and Z landmark coordinate. The authors had high ICC estimates within rates, indicating high consistency, and high ICC estimates among raters, indicate good agreement across raters. Overall measurement error for each landmark and each X, Y, and Z coordinate was low. Our results confirm the accuracy of novel 3D landmarks including several on the cranial base that will serve researchers and clinicians for use in future studies involving 3D CBCT imaging and craniofacial development.

Comparison of Three-Dimensional Surface Imaging Systems Using Landmark Analysis.

Numerous 3d imaging systems are now available commercially for the capture of facial shape data via landmarking or surface shape comparisons but it is not known whether these systems produce data of comparable quality. This study investigates the error associated with landmark coordinate data collected on facial surface images taken using three 3d imaging systems: the 3dMDface system (3dMD, Atlanta, GA), the Planmeca ProFace system (Planmeca, Roselle, IL), and the Vectra H1 handheld system (Canfield Scientific, Parsippany, NJ). This was a retrospective study involving 3d imaging data that used geometric morphometric analysis to assess overall shape differences as well as landmark-specific differences among the systems. Ten individuals evaluated at the NIDCR dental clinic on various protocols were imaged on all 3 systems. The subject pool consisted of syndromic and unaffected subjects, as disease status was irrelevant to the question of reproducibility and variability. Variation in landmark placement across systems was assessed by ANOVA, covariance matrix, and summary statistics. No overall shape or size differences were found among the systems. However, there was some landmark-specific variation and the 3dMD and Vectra systems were generally more similar to each other than either was to the Planmeca system. The data acquired by these 3 systems are comparable, although landmarks on the eyes and ears are noisy and most different among systems.

Deep Geodesic Learning for Segmentation and Anatomical Landmarking.

In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In the first step, we propose a deep neural network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex post-processing refinement. In the second step, we formulate the landmark localization problem directly on the geodesic space for sparsely-spaced anatomical landmarks. In the third step, we utilize a long short-term memory network to identify the closely-spaced landmarks, which is rather difficult to obtain using other standard networks. The proposed fully automated method showed superior efficacy compared to the state-of-the-art mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT data set of 50 patients with a high-degree of craniomaxillofacial variability that is realistic in clinical practice. The qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown the state-of-the-art performance in an independent data set from the MICCAI Head-Neck Challenge (2015).

Facial shape manifestations of growth faltering in Tanzanian children.

Variation in the shape of the human face and in stature is determined by complex interactions between genetic and environmental influences. One such environmental influence is malnourishment, which can result in growth faltering, usually diagnosed by means of comparing an individual's stature with a set of age-appropriate standards. These standards for stature, however, are typically ascertained in groups where people are at low risk for growth faltering. Moreover, genetic differences among populations with respect to stature are well established, further complicating the generalizability of stature-based diagnostic tools. In a large sample of children aged 5-19 years, we obtained high-resolution genomic data, anthropometric measures and 3D facial images from individuals within and around the city of Mwanza, Tanzania. With genome-wide complex trait analysis, we partitioned genetic and environmental variance for growth outcomes and facial shape. We found that children with growth faltering have faces that look like those of older and taller children, in a direction opposite to the expected allometric trajectory, and in ways predicted by the environmental portion of covariance at the community and individual levels. The environmental variance for facial shape varied subtly but significantly among communities, whereas genetic differences were minimal. These results reveal that facial shape preserves information about exposure to undernourishment, with important implications for refining assessments of nutritional status in children and the developmental-genetics of craniofacial variation alike.

Body size and allometric variation in facial shape in children.

OBJECTIVES: Morphological integration, or the tendency for covariation, is commonly seen in complex traits such as the human face. The effects of growth on shape, or allometry, represent a ubiquitous but poorly understood axis of integration. We address the question of to what extent age and measures of size converge on a single pattern of allometry for human facial shape. METHODS: Our study is based on two large cross-sectional cohorts of children, one from Tanzania and the other from the United States (N = 7,173). We employ 3D facial imaging and geometric morphometrics to relate facial shape to age and anthropometric measures. RESULTS: The two populations differ significantly in facial shape, but the magnitude of this difference is small relative to the variation within each group. Allometric variation for facial shape is similar in both populations, representing a small but significant proportion of total variation in facial shape. Different measures of size are associated with overlapping but statistically distinct aspects of shape variation. Only half of the size-related variation in facial shape can be explained by the first principal component of four size measures and age while the remainder associates distinctly with individual measures. CONCLUSIONS: Allometric variation in the human face is complex and should not be regarded as a singular effect. This finding has important implications for how size is treated in studies of human facial shape and for the developmental basis for allometric variation more generally.

Rapid automated landmarking for morphometric analysis of three-dimensional facial scans.

Automated phenotyping is essential for the creation of large, highly standardized datasets from anatomical imaging data. Such datasets can support large-scale studies of complex traits or clinical studies related to precision medicine or clinical trials. We have developed a method that generates three-dimensional landmark data that meet the requirements of standard geometric morphometric analyses. The method is robust and can be implemented without high-performance computing resources. We validated the method using both direct comparison to manual landmarking on the same individuals and also analyses of the variation patterns and outlier patterns in a large dataset of automated and manual landmark data. Direct comparison of manual and automated landmarks reveals that automated landmark data are less variable, but more highly integrated and reproducible. Automated data produce covariation structure that closely resembles that of manual landmarks. We further find that while our method does produce some landmarking errors, they tend to be readily detectable and can be fixed by adjusting parameters used in the registration and control-point steps. Data generated using the method described here have been successfully used to study the genomic architecture of facial shape in two different genome-wide association studies of facial shape.

Human Facial Shape and Size Heritability and Genetic Correlations.

The human face is an array of variable physical features that together make each of us unique and distinguishable. Striking familial facial similarities underscore a genetic component, but little is known of the genes that underlie facial shape differences. Numerous studies have estimated facial shape heritability using various methods. Here, we used advanced three-dimensional imaging technology and quantitative human genetics analysis to estimate narrow-sense heritability, heritability explained by common genetic variation, and pairwise genetic correlations of 38 measures of facial shape and size in normal African Bantu children from Tanzania. Specifically, we fit a linear mixed model of genetic relatedness between close and distant relatives to jointly estimate variance components that correspond to heritability explained by genome-wide common genetic variation and variance explained by uncaptured genetic variation, the sum representing total narrow-sense heritability. Our significant estimates for narrow-sense heritability of specific facial traits range from 28 to 67%, with horizontal measures being slightly more heritable than vertical or depth measures. Furthermore, for over half of facial traits, >90% of narrow-sense heritability can be explained by common genetic variation. We also find high absolute genetic correlation between most traits, indicating large overlap in underlying genetic loci. Not surprisingly, traits measured in the same physical orientation (i.e., both horizontal or both vertical) have high positive genetic correlations, whereas traits in opposite orientations have high negative correlations. The complex genetic architecture of facial shape informs our understanding of the intricate relationships among different facial features as well as overall facial development.

Genomewide Association Study of African Children Identifies Association of SCHIP1 and PDE8A with Facial Size and Shape.

The human face is a complex assemblage of highly variable yet clearly heritable anatomic structures that together make each of us unique, distinguishable, and recognizable. Relatively little is known about the genetic underpinnings of normal human facial variation. To address this, we carried out a large genomewide association study and two independent replication studies of Bantu African children and adolescents from Mwanza, Tanzania, a region that is both genetically and environmentally relatively homogeneous. We tested for genetic association of facial shape and size phenotypes derived from 3D imaging and automated landmarking of standard facial morphometric points. SNPs within genes SCHIP1 and PDE8A were associated with measures of facial size in both the GWAS and replication cohorts and passed a stringent genomewide significance threshold adjusted for multiple testing of 34 correlated traits. For both SCHIP1 and PDE8A, we demonstrated clear expression in the developing mouse face by both whole-mount in situ hybridization and RNA-seq, supporting their involvement in facial morphogenesis. Ten additional loci demonstrated suggestive association with various measures of facial shape. Our findings, which differ from those in previous studies of European-derived whites, augment understanding of the genetic basis of normal facial development, and provide insights relevant to both human disease and forensics.

Genetics of murine craniofacial morphology: diallel analysis of the eight founders of the Collaborative Cross.

Using eight inbred founder strains of the mouse Collaborative Cross (CC) project and their reciprocal F1 hybrids, we quantified variation in craniofacial morphology across mouse strains, explored genetic contributions to craniofacial variation that distinguish the founder strains, and tested whether specific or summary measures of craniofacial shape display stronger additive genetic contributions. This study thus provides critical information about phenotypic diversity among CC founder strains and about the genetic contributions to this phenotypic diversity, which is relevant to understanding the basis of variation in standard laboratory strains and natural populations. Craniofacial shape was quantified as a series of size-adjusted linear dimensions (RDs) and by principal components (PC) analysis of morphological landmarks captured from computed tomography images from 62 of the 64 reciprocal crosses of the CC founder strains. We first identified aspects of skull morphology that vary between these phenotypically 'normal' founder strains and that are defining characteristics of these strains. We estimated the contributions of additive and various non-additive genetic factors to phenotypic variation using diallel analyses of a subset of these strongly differing RDs and the first eight PCs of skull shape variation. We find little difference in the genetic contributions to RD measures and PC scores, suggesting fundamental similarities in the magnitude of genetic contributions to both specific and summary measures of craniofacial phenotypes. Our results indicate that there are stronger additive genetic effects associated with defining phenotypic characteristics of specific founder strains, suggesting these distinguishing measures are good candidates for use in genotype-phenotype association studies of CC mice. Our results add significantly to understanding of genotype-phenotype associations in the skull, which serve as a foundation for modeling the origins of medically and evolutionarily relevant variation.

Craniofacial morphometric analysis of individuals with X-linked hypohidrotic ectodermal dysplasia.

Hypohidrotic ectodermal dysplasia (HED) is the most prevalent type of ectodermal dysplasia (ED). ED is an umbrella term for a group of syndromes characterized by missing or malformed ectodermal structures, including skin, hair, sweat glands, and teeth. The X-linked recessive (XL), autosomal recessive (AR), and autosomal dominant (AD) types of HED are caused by mutations in the genes encoding ectodysplasin (EDA1), EDA receptor (EDAR), or EDAR-associated death domain (EDARADD). Patients with HED have a distinctive facial appearance, yet a quantitative analysis of the HED craniofacial phenotype using advanced three-dimensional (3D) technologies has not been reported. In this study, we characterized craniofacial morphology in subjects with X-linked hypohidrotic ectodermal dysplasia (XLHED) by use of 3D imaging and geometric morphometrics (GM), a technique that uses defined landmarks to quantify size and shape in complex craniofacial morphologies. We found that the XLHED craniofacial phenotype differed significantly from controls. Patients had a smaller and shorter face with a proportionally longer chin and midface, prominent midfacial hypoplasia, a more protrusive chin and mandible, a narrower and more pointed nose, shorter philtrum, a narrower mouth, and a fuller and more rounded lower lip. Our findings refine the phenotype of XLHED and may be useful both for clinical diagnosis of XLHED and to extend understanding of the role of EDA in craniofacial development.

Facial morphometrics of children with non-syndromic orofacial clefts in Tanzania.

BACKGROUND: Orofacial clefts (cleft lip/palate; CL/P) are among the most common congenital anomalies, with prevalence that varies among different ethnic groups. Craniofacial shape differences between individuals with CL/P and healthy controls have been widely reported in non-African populations. Knowledge of craniofacial shape among individuals with non-syndromic CL/P in African populations will provide further understanding of the ethnic and phenotypic variation present in non-syndromic orofacial clefts. METHODS: A descriptive cross-sectional study was carried out at Bugando Medical Centre, Tanzania, comparing individuals with unrepaired non-syndromic CL/P and normal individuals without orofacial clefts. Three-dimensional (3D) facial surfaces were captured using a non-invasive 3D camera. The corresponding 3D coordinates for 26 soft tissue landmarks were used to characterize facial shape. Facial shape variation within and between groups, based on Procrustes superimposed data, was studied using geometric morphometric methods. RESULTS: Facial shape of children with cleft lip differed significantly from the control group, beyond the cleft itself. The CL/P group exhibited increased nasal and mouth width, increased interorbital distance, and more prognathic premaxillary region. Within the CL/P group, PCA showed that facial shape variation is associated with facial height, nasal cavity width, interorbital distance and midfacial prognathism. The isolated cleft lip (CL) and combined cleft lip and palate (CLP) groups did not differ significantly from one another (Procrustes distance = 0.0416, p = 0.50). Procrustes distance permutation tests within the CL/P group showed a significant shape difference between unilateral clefts and bilateral clefts (Procrustes distance = 0.0728, p = 0.0001). Our findings indicate the morphological variation is similar to those of studies of CL/P patients and their unaffected close relatives in non-African populations. CONCLUSION: The mean facial shape in African children with non-syndromic CL/P differs significantly from children without orofacial clefts. The main differences involve interorbital width, facial width and midface prognathism. The axes of facial shape differences we observed are similar to the patterns seen in Caucasian populations, despite apparent differences in cleft prevalence and cleft type distribution. Similar facial morphology in individuals with CL/P in African and Caucasian populations suggests a similar aetiology.

Modeling 3D facial shape from DNA.

Human facial diversity is substantial, complex, and largely scientifically unexplained. We used spatially dense quasi-landmarks to measure face shape in population samples with mixed West African and European ancestry from three locations (United States, Brazil, and Cape Verde). Using bootstrapped response-based imputation modeling (BRIM), we uncover the relationships between facial variation and the effects of sex, genomic ancestry, and a subset of craniofacial candidate genes. The facial effects of these variables are summarized as response-based imputed predictor (RIP) variables, which are validated using self-reported sex, genomic ancestry, and observer-based facial ratings (femininity and proportional ancestry) and judgments (sex and population group). By jointly modeling sex, genomic ancestry, and genotype, the independent effects of particular alleles on facial features can be uncovered. Results on a set of 20 genes showing significant effects on facial features provide support for this approach as a novel means to identify genes affecting normal-range facial features and for approximating the appearance of a face from genetic markers.

Fine tuning of craniofacial morphology by distant-acting enhancers.

The shape of the human face and skull is largely genetically determined. However, the genomic basis of craniofacial morphology is incompletely understood and hypothesized to involve protein-coding genes, as well as gene regulatory sequences. We used a combination of epigenomic profiling, in vivo characterization of candidate enhancer sequences in transgenic mice, and targeted deletion experiments to examine the role of distant-acting enhancers in craniofacial development. We identified complex regulatory landscapes consisting of enhancers that drive spatially complex developmental expression patterns. Analysis of mouse lines in which individual craniofacial enhancers had been deleted revealed significant alterations of craniofacial shape, demonstrating the functional importance of enhancers in defining face and skull morphology. These results demonstrate that enhancers are involved in craniofacial development and suggest that enhancer sequence variation contributes to the diversity of human facial morphology.