Craniofacial reconstruction as a prediction problem using a Latent Root Regression model

International audienceIn this paper, we present a computer-assisted method for facial reconstruction. This method provides an estimation of the facial shape associated with unidentified skeletal remains. Current computer-assisted methods using a statistical framework rely on a common set of extracted points located on the bone and soft-tissue surfaces. Most of the facial reconstruction methods then consist of predicting the position of the soft-tissue surface points, when the positions of the bone surface points are known. We propose to use Latent Root Regression for prediction. The results obtained are then compared to those given by Principal Components Analysis linear models. In conjunction, we have evaluated the influence of the number of skull landmarks used. Anatomical skull landmarks are completed iteratively by points located upon geodesics which link these anatomical landmarks, thus enabling us to artificially increase the number of skull points. Facial points are obtained using a mesh-matching algorithm between a common reference mesh and individual soft-tissue surface meshes. The proposed method is validated in term of accuracy, based on a leave-one-out cross-validation test applied to a homogeneous database. Accuracy measures are obtained by computing the distance between the original face surface and its reconstruction. Finally, these results are discussed referring to current computer-assisted reconstruction facial techniques

Physical and statistical shape modelling in craniomaxillofacial surgery: a personalised approach for outcome prediction

Orthognathic surgery involves repositioning of the jaw bones to restore face function and shape for patients who require an operation as a result of a syndrome, due to growth disturbances in childhood or after trauma. As part of the preoperative assessment, three-dimensional medical imaging and computer-assisted surgical planning help to improve outcomes, and save time and cost. Computer-assisted surgical planning involves visualisation and manipulation of the patient anatomy and can be used to aid objective diagnosis, patient communication, outcome evaluation, and surgical simulation. Despite the benefits, the adoption of three-dimensional tools has remained limited beyond specialised hospitals and traditional two-dimensional cephalometric analysis is still the gold standard. This thesis presents a multidisciplinary approach to innovative surgical simulation involving clinical patient data, medical image analysis, engineering principles, and state-of-the-art machine learning and computer vision algorithms. Two novel three-dimensional computational models were developed to overcome the limitations of current computer-assisted surgical planning tools. First, a physical modelling approach – based on a probabilistic finite element model – provided patient-specific simulations and, through training and validation, population-specific parameters. The probabilistic model was equally accurate compared to two commercial programs whilst giving additional information regarding uncertainties relating to the material properties and the mismatch in bone position between planning and surgery. Second, a statistical modelling approach was developed that presents a paradigm shift in its modelling formulation and use. Specifically, a 3D morphable model was constructed from 5,000 non-patient and orthognathic patient faces for fully-automated diagnosis and surgical planning. Contrary to traditional physical models that are limited to a finite number of tests, the statistical model employs machine learning algorithms to provide the surgeon with a goal-driven patient-specific surgical plan. The findings in this thesis provide markers for future translational research and may accelerate the adoption of the next generation surgical planning tools to further supplement the clinical decision-making process and ultimately to improve patients’ quality of life

The development of bite force resistance and cranial form in Neanderthals and modern humans

The general aim of the thesis is to understand how biting mechanics interact with cranial form to impact post-natal craniofacial ontogeny in modern humans and Neander-thals. To this end, CT scans of ontogenetic samples of 12 Neanderthal and 63 modern human crania were collected and a series of reconstructions of Neanderthal crania were carried out. Geometric morphometric and multivariate regression approaches were used to create a craniofacial growth model for each species. Using these two models, 3D virtual crania representing the mean adult, juvenile, and infant were extracted in each species. These 6 mean crania were then converted into finite element models and used to conduct two biting simulations: at the right second premolar or second deciduous molar (RP2/RdM2) and right first incisor (RI1), applying the same muscle forces for all models because these are unknown especially for Neanderthals. This study compared modes and magnitudes of deformation, and the distribution and magnitude of tensile and compres-sive strains between the mean infant, juvenile, and adult models within each species and between the two species at each age stage.The morphometric analyses indicate that cranial ontogenetic trajectories differ be-tween modern humans and Neanderthals. The finite element analyses (FEA) in both bit-ing simulations indicate that, within each species, the mean infant juvenile and adult mod-els deform differently. Further, in both biting simulations, the highest strains are localised over similar regions of the cranium; over the anterior maxilla, orbits, and anterior subna-sal surface. Modern humans and Neanderthals deform differently and show differences in the development of biting forces during RI1 and RP2/RdM2 biting simulations at each stage. These findings confirm that modern human and Neanderthal crania have divergent postnatal developmental trajectories and manifest differences in the resistance of masti-catory system loadings throughout life. Differences in modes of deformation and so, strain distributions are considered in light of known differences in craniofacial bone growth remodeling between Neanderthals and modern humans. The findings show some correspondence with the remodeling maps for both species, particularly during RP2/RdM2 biting simulations. They do not falsify the hypothesis that facial remodeling differences arise because of differences in load resistance, and so, in the strain environment during post-natal development. As such, how differences among adult crania arise through post-natal interactions between form and functional loadings merits further investigation through more detailed analyses of a wider range of loading scenarios

DICTIONARIES AND MANIFOLDS FOR FACE RECOGNITION ACROSS ILLUMINATION, AGING AND QUANTIZATION

During the past many decades, many face recognition algorithms have been proposed. The face recognition problem under controlled environment has been well studied and almost solved. However, in unconstrained environments, the performance of face recognition methods could still be significantly affected by factors such as illumination, pose, resolution, occlusion, aging, etc. In this thesis, we look into the problem of face recognition across these variations and quantization. We present a face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose with dictionaries learned for each class. A novel test image is projected onto the span of the atoms in each learned dictionary. The resulting residual vectors are then used for classification. An image relighting technique based on pose-robust albedo estimation is used to generate multiple frontal images of the same person with variable lighting. As a result, the proposed algorithm has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training. The efficiency of the proposed method is demonstrated using publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms. The problem of recognizing facial images across aging remains an open problem. We look into this problem by studying the growth in the facial shapes. Building on recent advances in landmark extraction, and statistical techniques for landmark-based shape analysis, we show that using well-defined shape spaces and its associated geometry, one can obtain significant performance improvements in face verification. Toward this end, we propose to model the facial shapes as points on a Grassmann manifold. The face verification problem is then formulated as a classification problem on this manifold. We then propose a relative craniofacial growth model which is based on the science of craniofacial anthropometry and integrate it with the Grassmann manifold and the SVM classifier. Experiments show that the proposed method is able to mitigate the variations caused by the aging progress and thus effectively improve the performance of open-set face verification across aging. In applications such as document understanding, only binary face images may be available as inputs to a face recognition algorithm. We investigate the effects of quantization on several classical face recognition algorithms. We study the performances of PCA and multiple exemplar discriminant analysis (MEDA) algorithms with quantized images and with binary images modified by distance and Box-Cox transforms. We propose a dictionary-based method for reconstructing the grey scale facial images from the quantized facial images. Two dictionaries with low mutual coherence are learned for the grey scale and quantized training images respectively using a modified KSVD method. A linear transform function between the sparse vectors of quantized images and the sparse vectors of grey scale images is estimated using the training data. In the testing stage, a grey scale image is reconstructed from the quantized image using the transform matrix and normalized dictionaries. The identities of the reconstructed grey scale images are then determined using the dictionary-based face recognition (DFR) algorithm. Experimental results show that the reconstructed images are similar to the original grey-scale images and the performance of face recognition on the quantized images is comparable to the performance on grey scale images. The online social network and social media is growing rapidly. It is interesting to study the impact of social network on computer vision algorithms. We address the problem of automated face recognition on a social network using a loopy belief propagation framework. The proposed approach propagates the identities of faces in photos across social graphs. We characterize its performance in terms of structural properties of the given social network. We propose a distance metric defined using face recognition results for detecting hidden connections. The performance of the proposed method is analyzed on graph structure networks, scalability, different degrees of nodes, labeling errors correction and hidden connections discovery. The result demonstrates that the constraints imposed by the social network have the potential to improve the performance of face recognition methods. The result also shows it is possible to discover hidden connections in a social network based on face recognition

A finite element study of the human cranium : the impact of morphological variation on biting performance

This thesis investigated the relationship between craniofacial morphology and masticatory mechanics using finite element analysis (FEA). Chapter 1 is a literature review of the relevant background: bone mechanics, jaw-elevator muscle anatomy, imaging techniques, FEA and geometric morphometrics.The second, third and fourth chapters comprise experimental work aiming to provide a framework for FE model construction and loading. The second chapter aimed to validate the method for FE model building and assess the sensitivity of models to simplifications. Models with simplified bone anatomy and resolution predicted strains close to those measured experimentally. The third chapter assessed the predictability of muscle cross-sectional area (CSA) from bony features. It was found that muscle CSA, an estimator of muscle force, has low predictability. The fourth chapter assessed FE model sensitivity to variations in applied muscle forces. Results showed that a cranial FE model behaved reasonably robustly under variations in the muscle loading regimen.Chapter 5 uses the conclusions from the previous studies to build FE models of six human crania, including two individuals with artificial deformations of the neurocranium. Despite differences in form and the presence of deformation, all performed similarly during biting, varying mainly in the magnitudes of performance parameters. The main differences related to the form of the maxilla, irrespective of neurocranial deformation. The most orthognatic individuals with the narrowest maxilla showed the most distinctive deformation during incisor and molar bites, and achieved the greatest bite force efficiency. However, bite forces were similar among individuals irrespective of the presence of artificial deformation. This appears to relate to the preservation of normal dental occlusion, which in turn maintains similar loading and so morphogenesis of the mid face. Altogether, the results of this thesis show that FEA is reliable in comparing masticatory system functioning and point to how variations in morphology impact skeletal performance

Recent hominim cranial form and function

This thesis aims to assess if biting mechanics drives craniofacial morphology in recent hominins. To that end, a virtual functional morphology toolkit, that includes Finite Element Analysis (FEA) and Geometric Morphometrics (GM), is used to simulate biting, measure bite force and quantify deformations arising due to simulated biting in Homo sapiens and its proposed ancestral species, Homo heidelbergensis. Moreover, the mechanical significance of the frontal sinus and of the brow-ridge is also assessed in Kabwe 1 (a Homo heidelbergensis specimen). The frontal sinus is examined by comparing the mechanical performance in three FE models with varying sinus morphology. A similar approach is applied to the brow-ridge study. This approach relies on the assumption that FEA approximates reality. Thus, a validation study compares the deformations experienced by a real cranium under experimental loading with those experienced by an FE model under equivalent virtual loading to verify this assumption. A sensitivity analysis examines how simplifications in segmentation impact on FEA results. Lastly, the virtual reconstruction of Kabwe 1 is described.Results show that prediction of absolute strain magnitudes is not precise, but the distribution of regions of larger and smaller (i.e. pattern of) deformations experienced by the real cranium is reasonably approximated by FEA, despite discrepancies in the alveolus. Simplification of segmentation stiffens the model but has no impact on the pattern of deformations, with the exception of the alveolus. Comparison of the biting performance of Kabwe 1 and H. sapiens suggests that morphological differences between the two species are likely not driven by selection of the masticatory system. Frontal sinus morphogenesis and morphology are possibly impacted by biting mechanics in the sense that very low strains are experienced by this region. Because bone adapts to strains, the frontal sinus is possibly impacted by this mechanism. Lastly, biting mechanics has limited impact on brow-ridge morphology and does not explain fully the enormous brow-ridge of Kabwe 1. Hence, other explanations are necessary to explain this prominent feature

Anyone with a Long-Face? Craniofacial Evolutionary Allometry (CREA) in a Family of Short-Faced Mammals, the Felidae

Among adults of closely related species, a trend in craniofacial evolutionary allometry (CREA) for larger taxa to be long-faced and smaller ones to have paedomorphic aspects, such as proportionally smaller snouts and larger braincases, has been demonstrated in some mammals and two bird lineages. Nevertheless, whether this may represent a ‘rule’ with few exceptions is still an open question. In this context, Felidae is a particularly interesting family to study because, although its members are short-faced, previous research did suggest relative facial elongation in larger living representatives. Using geometric morphometrics, based on two sets of anatomical landmarks, and traditional morphometrics, for comparing relative lengths of the palate and basicranium, we performed a series of standard and comparative allometric regressions in the Felidae and its two subfamilies. All analyses consistently supported the CREA pattern, with only one minor exception in the geometric morphometric analysis of Pantherinae: the genus Neofelis. With its unusually long canines, Neofelis species seem to have a relatively narrow cranium and long face, despite being smaller than other big cats. In spite of this, overall, our findings strengthen the possibility that the CREA pattern might indeed be a ‘rule’ among mammals, raising questions on the processes behind it and suggesting future directions for its study

Craniofacial integration, plasticity and biomechanics in the mouse masticatory system

The craniomandibular skeleton is a complex, dynamic structure, housing many vital tissues and required to perform critical functions. This region is however subject to substantial morphological change during development, and required to adapt to its environment and individual variance. The capacity of this region to maintain correlated form and appropriate functional performance despite these challenges is not fully understood. The sample consists of three strains of mice; a wild-type strain and two mutant strains from the same genetic background strain. Both mutations selectively affect chondrocranial growth, and thus influence of both are limited to the crania. The brachymorph mutant phenotype is characterised by a shortened cranium, while the pten is elongated. This sample therefore allows exploration of a potential plastic response in terms of the mandible, the masticatory lever system, and in turn mechanical advantage, when cranial length and the out-lever are varied. Three dimensional landmarks were applied to micro-CT scans and partial-least-squares analysis carried out to determine covariance between crania and mandibles. Mechanical advantage was calculated as a ratio of muscle in-lever and jaw out-lever for three key masticatory muscles. A common pattern of both variance and covariance was found among all three strains, with mandibular morphology in each strain covarying with cranial phenotypes. Jaw out-lever lengths were found to be significantly different in all three strains, and yet little significant difference between strains was found in mechanical advantage for any muscles. This maintenance of mechanical advantage is attributed to plastic adaptation in regions influencing muscle in-lever length, the latter which were found to be significantly different in the three strains. These results show the potential of the craniomandibular complex to plastically adapt to maintain both correlated form and functionality when variation occurs in one region, and thus these results have significant implications for the evolvability of the complex

A computerized craniofacial reconstruction method for an unidentified skull based on statistical shape models

Craniofacial reconstruction (CFR) has been widely used to produce the facial appearance of an unidentified skull in the realm of forensic science. Many studies have indicated that the computerized CFR approach is fast, flexible, consistent and objective in comparison to the traditional manual CFR approach. This paper presents a computerized CFR system called CFRTools, which features a CFR method based on a statistical shape model (SSM) of living human head models. Given an unidentified skull, a geometrically-similar template skull is chosen as a template, and a non-registration method is used to improve the accuracy of the construction of dense corresponding vertices through the alignment of the template and the unidentified skull. Generalized Procrustes analysis (GPA) and principal component analysis (PCA) are carried out to construct the skull and face SSMs. The sex of the unidentified skull is then predicted based on skull SSM and centroid size, rather than geometric measurements based on anatomical landmarks. Furthermore, a craniofacial morphological relationship which is learnt from the principal component (PC) scores of the skull and face dataset is used to produce a possible reconstructed face. Finally, multiple possible reconstructed faces for the same skull can further be recreated based on adjusting the PC coefficients. The experimental results show that the average rate of sex classification is 97.14% and the reconstructed face of the unidentified skull can be produced. In addition, experts’ understanding and experience can be harnessed in production of face variations for the same skull, which can further be used as a reference for portraiture creation