Coordinate Systems Integration for Craniofacial Database from Multimodal Devices

This study presents a data registration method for craniofacial spatial data of different modalities. The data consists of three dimensional (3D) vector and raster data models. The data is stored in object relational database. The data capture devices are Laser scanner, CT (Computed Tomography) scan and CR (Close Range) Photogrammetry. The objective of the registration is to transform the data from various coordinate systems into a single 3-D Cartesian coordinate system. The standard error of the registration obtained from multimodal imaging devices using 3D affine transformation is in the ranged of 1-2 mm. This study is a step forward for storing the craniofacial spatial data in one reference system in database

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

Evolution of design considerations in complex craniofacial reconstruction using patient-specific implants

Previously published evidence has established major clinical benefits from using Computer Aided Design (CAD), Computer Aided Manufacturing (CAM), and Additive Manufacturing (AM) to produce patient-specific devices. These include cutting guides, drilling guides, positioning guides, and implants. However, custom devices produced using these methods are still not in routine use – particularly by the UK National Health Service (NHS). Oft-cited reasons for this slow uptake include: a higher up-front cost than conventionally-fabricated devices, material-choice uncertainty, and a lack of long-term follow-up due to their relatively recent introduction. This paper identifies a further gap in current knowledge – that of design rules, or key specification considerations for complex CAD/CAM/AM devices. This research begins to address the gap by combining a detailed review of the literature with first-hand experience of interdisciplinary collaboration on five craniofacial patient case-studies. In each patient case, bony lesions in the orbito-temporal region were segmented, excised, and reconstructed in the virtual environment. Three cases translated these digital plans into theatre via polymer surgical guides. Four cases utilised AM to fabricate titanium implants. One implant was machined from PolyEther Ether Ketone (PEEK). From the literature, articles with relevant abstracts were analysed to extract design considerations. 19 frequently-recurring design considerations were extracted from previous publications. 9 new design considerations were extracted from the case studies – on the basis of subjective clinical evaluation. These were synthesised to produce a design considerations framework to assist clinicians with prescribing and design engineers with modelling. Promising avenues for further research are proposed

Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related 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

Craniofacial Syndrome Identification Using Convolutional Mesh Autoencoders

Background: 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. While these are powerful tools for photographic analysis, they 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. // Methods: 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). // Findings: 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 makes it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting. // Interpretation: Our study demonstrates the use of 3D convolutional mesh autoencoders for the diagnosis of syndromic craniosynostosis. The topological nature of the tool presents opportunities for this method to be applied as a diagnostic tool across a number of 3D imaging modalities

Three-dimensional virtual-reality surgical planning and soft-tissue prediction for orthognathic surgery

Complex maxillofacial malformations continue to present challenges in analysis and correction beyond modern technology. The purpose of this paper is to present a virtual-reality workbench for surgeons to perform virtual orthognathic surgical planning and soft-tissue prediction in three dimensions. A resulting surgical planning system, i.e., three-dimensional virtual-reality surgical-planning and soft-tissue prediction for orthognathic surgery, consists of four major stages: computed tomography (CT) data post-processing and reconstruction, three-dimensional (3-D) color facial soft-tissue model generation, virtual surgical planning and simulation, soft-tissue-change preoperative prediction. The surgical planning and simulation are based on a 3-D CT reconstructed bone model, whereas the soft-tissue prediction is based on color texture-mapped and individualized facial soft-tissue model. Our approach is able to provide a quantitative osteotomy-simulated bone model and prediction of postoperative appearance with photorealistic quality. The prediction appearance can be visualized from any arbitrary viewing point using a low-cost personal-computer-based system. This cost-effective solution can be easily adopted in any hospital for daily use.published_or_final_versio

Detailing patient specific modelling to aid clinical decision-making

The anatomy of the craniofacial skeleton has been described through the aid of dissection identifying hard and soft tissue structures. Although the macro and microscopic investigation of internal facial tissues have provided invaluable information on constitution of the tissues it is important to inspect and model facial tissues in the living individual. Detailing the form and function of facial tissues will be invaluable in clinical diagnoses and planned corrective surgical interventions such as management of facial palsies and craniofacial disharmony/anomalies. Recent advances in lower-cost, non-invasive imaging and computing power (surface scanning, Cone Beam Computerized Tomography (CBCT) and Magnetic Resonance (MRI)) has enabled the ability to capture and process surface and internal structures to a high resolution. The three-dimensional surface facial capture has enabled characterization of facial features all of which will influence subtleties in facial movement and surgical planning. This chapter will describe the factors that influence facial morphology in terms of gender and age differences, facial movement—surface and underlying structures, modeling based on average structures, orientation of facial muscle fibers, biomechanics of movement—proof of principle and surgical intervention

Latent Disentanglement for the Analysis and Generation of Digital Human Shapes

Analysing and generating digital human shapes is crucial for a wide variety of applications ranging from movie production to healthcare. The most common approaches for the analysis and generation of digital human shapes involve the creation of statistical shape models. At the heart of these techniques is the definition of a mapping between shapes and a low-dimensional representation. However, making these representations interpretable is still an open challenge. This thesis explores latent disentanglement as a powerful technique to make the latent space of geometric deep learning based statistical shape models more structured and interpretable. In particular, it introduces two novel techniques to disentangle the latent representation of variational autoencoders and generative adversarial networks with respect to the local shape attributes characterising the identity of the generated body and head meshes. This work was inspired by a shape completion framework that was proposed as a viable alternative to intraoperative registration in minimally invasive surgery of the liver. In addition, one of these methods for latent disentanglement was also applied to plastic surgery, where it was shown to improve the diagnosis of craniofacial syndromes and aid surgical planning