Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D shapes and textures of an object class. Here we present the most complete 3DMM of the human head to date that includes face, cranium, ears, eyes, teeth and tongue. To achieve this, we propose two methods for combining existing 3DMMs of different overlapping head parts: i. use a regressor to complete missing parts of one model using the other, ii. use the Gaussian Process framework to blend covariance matrices from multiple models. Thus we build a new combined face-and-head shape model that blends the variability and facial detail of an existing face model (the LSFM) with the full head modelling capability of an existing head model (the LYHM). Then we construct and fuse a highly-detailed ear model to extend the variation of the ear shape. Eye and eye region models are incorporated into the head model, along with basic models of the teeth, tongue and inner mouth cavity. The new model achieves state-of-the-art performance. We use our model to reconstruct full head representations from single, unconstrained images allowing us to parameterize craniofacial shape and texture, along with the ear shape, eye gaze and eye color.
Background Many treatments aim to slow down or reverse the visible signs of skin aging and thereby improve skin quality. Measurement devices are frequently employed to measure the effects of these treatments to improve skin quality, for example, skin elasticity, color, and texture. However, it remains unknown which of these devices is most reliable and valid. Materials and methods MEDLINE, Embase, Cochrane Central, Web of Science, and Google Scholar databases were searched. Instruments were scored on reporting construct validity by means of convergent validity, interobserver, intraobserver, and interinstrument reliability. Results For the evaluation of skin color, 11 studies were included describing 16 measurement devices, analyzing 3172 subjects. The most reliable device for skin color assessment is the Minolta Chromameter CR‐300 due to good interobserver, intraobserver, and interinstrument reliability. For skin elasticity, seven studies assessed nine types of devices analyzing 290 subjects in total. No intra and interobserver reliability was reported. Skin texture was assessed in two studies evaluating 72 subjects using three different types of measurement devices. The PRIMOS device reported excellent intra and interobserver reliability. None of the included reviewed devices could be determined to be valid based on construct validity. Conclusion The most reliable devices to evaluate skin color and texture in ordinary skin were, respectively, the Minolta Chromameter and PRIMOS. No reliable device is available to measure skin elasticity in ordinary skin and none of the included devices could be determined to be designated as valid.
Crouzon syndrome is characterized by multiple craniofacial suture fusions leading to skull deformation and midfacial hypoplasia. Although there is a degree of phenotypic variance, Background: Crouzon syndrome is characterized by complex craniosynostosis and midfacial hypoplasia. Where frontofacial monobloc advancement (FFMBA) is indicated, the method of distraction used to achieve advancement holds an element of equipoise. This two-center retrospective cohort study quantifies the movements produced by internal or external distraction methods used for FFMBA. Using shape analysis, this study evaluates whether the different distraction forces cause plastic deformity of the frontofacial segment, producing distinct morphologic outcomes. Methods: Patients with Crouzon syndrome who underwent FFMBA with internal distraction [Hôpital Necker-Enfants Malades (Paris, France)] or external distraction [Great Ormond Street Hospital for Children (London, United Kingdom)] were compared. Digital Imaging and Communications in Medicine files of preoperative and postoperative computed tomographic scans were converted to three-dimensional bone meshes and skeletal movements were assessed using nonrigid iterative closest point registration. Displacements were visualized using color maps and statistical analysis of the vectors was undertaken.Results: Fifty-one patients met the strict inclusion criteria. Twenty-five underwent FFMBA with external distraction and 26 with internal distraction. External distraction provides a preferential midfacial advancement, whereas internal distractors produce a more positive movement at the lateral orbital rim. This confers good orbital protection but does not advance the central midface to the same extent. Vector analysis confirmed this to be statistically significant (P < 0.01). Conclusions: Morphologic changes resulting from monobloc surgery differ depending on the distraction technique used. Although the relative merits of internal and external distraction still stand, it may be that external distraction is more suited to addressing the midfacial biconcavity seen in syndromic craniosynostosis.
Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture and colour. They are powerful tools for photographic analysis but are not suitable for use with medical imaging modalities such as ultrasound, MRI or CT, and are unable to take shape information into consideration when making a diagnostic prediction. 3D morphable models (3DMMs), and their recently proposed successors, mesh autoencoders, analyse surface topography rather than texture enabling analysis from photography and all common medical imaging modalities and present an alternative to image-based analysis. We present a craniofacial analysis framework for syndrome identification using Convolutional Mesh Autoencoders (CMAs). The models were trained using 3D photographs of the general population (LSFM and LYHM), computed tomography data (CT) scans from healthy infants and patients with 3 genetically distinct craniofacial syndromes (Muenke, Crouzon, Apert). Machine diagnosis outperformed expert clinical diagnosis with an accuracy of 99.98%, sensitivity of 99.95% and specificity of 100%. The diagnostic precision of this technique supports its potential inclusion in clinical decision support systems. Its reliance on 3D topography characterisation make it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting.
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