Development, Implementation and Pre-clinical Evaluation of Medical Image Computing Tools in Support of Computer-aided Diagnosis: Respiratory, Orthopedic and Cardiac Applications

Over the last decade, image processing tools have become crucial components of all clinical and research efforts involving medical imaging and associated applications. The imaging data available to the radiologists continue to increase their workload, raising the need for efficient identification and visualization of the required image data necessary for clinical assessment. Computer-aided diagnosis (CAD) in medical imaging has evolved in response to the need for techniques that can assist the radiologists to increase throughput while reducing human error and bias without compromising the outcome of the screening, diagnosis or disease assessment. More intelligent, but simple, consistent and less time-consuming methods will become more widespread, reducing user variability, while also revealing information in a more clear, visual way. Several routine image processing approaches, including localization, segmentation, registration, and fusion, are critical for enhancing and enabling the development of CAD techniques. However, changes in clinical workflow require significant adjustments and re-training and, despite the efforts of the academic research community to develop state-of-the-art algorithms and high-performance techniques, their footprint often hampers their clinical use. Currently, the main challenge seems to not be the lack of tools and techniques for medical image processing, analysis, and computing, but rather the lack of clinically feasible solutions that leverage the already developed and existing tools and techniques, as well as a demonstration of the potential clinical impact of such tools. Recently, more and more efforts have been dedicated to devising new algorithms for localization, segmentation or registration, while their potential and much intended clinical use and their actual utility is dwarfed by the scientific, algorithmic and developmental novelty that only result in incremental improvements over already algorithms. In this thesis, we propose and demonstrate the implementation and evaluation of several different methodological guidelines that ensure the development of image processing tools --- localization, segmentation and registration --- and illustrate their use across several medical imaging modalities --- X-ray, computed tomography, ultrasound and magnetic resonance imaging --- and several clinical applications: Lung CT image registration in support for assessment of pulmonary nodule growth rate and disease progression from thoracic CT images. Automated reconstruction of standing X-ray panoramas from multi-sector X-ray images for assessment of long limb mechanical axis and knee misalignment. Left and right ventricle localization, segmentation, reconstruction, ejection fraction measurement from cine cardiac MRI or multi-plane trans-esophageal ultrasound images for cardiac function assessment. When devising and evaluating our developed tools, we use clinical patient data to illustrate the inherent clinical challenges associated with highly variable imaging data that need to be addressed before potential pre-clinical validation and implementation. In an effort to provide plausible solutions to the selected applications, the proposed methodological guidelines ensure the development of image processing tools that help achieve sufficiently reliable solutions that not only have the potential to address the clinical needs, but are sufficiently streamlined to be potentially translated into eventual clinical tools provided proper implementation. G1: Reducing the number of degrees of freedom (DOF) of the designed tool, with a plausible example being avoiding the use of inefficient non-rigid image registration methods. This guideline addresses the risk of artificial deformation during registration and it clearly aims at reducing complexity and the number of degrees of freedom. G2: The use of shape-based features to most efficiently represent the image content, either by using edges instead of or in addition to intensities and motion, where useful. Edges capture the most useful information in the image and can be used to identify the most important image features. As a result, this guideline ensures a more robust performance when key image information is missing. G3: Efficient method of implementation. This guideline focuses on efficiency in terms of the minimum number of steps required and avoiding the recalculation of terms that only need to be calculated once in an iterative process. An efficient implementation leads to reduced computational effort and improved performance. G4: Commence the workflow by establishing an optimized initialization and gradually converge toward the final acceptable result. This guideline aims to ensure reasonable outcomes in consistent ways and it avoids convergence to local minima, while gradually ensuring convergence to the global minimum solution. These guidelines lead to the development of interactive, semi-automated or fully-automated approaches that still enable the clinicians to perform final refinements, while they reduce the overall inter- and intra-observer variability, reduce ambiguity, increase accuracy and precision, and have the potential to yield mechanisms that will aid with providing an overall more consistent diagnosis in a timely fashion

CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

Optical Cryoimaging of Tissue Metabolism in Renal Injuries: Rodent Model

Injured tissues are often accompanied by morphological or biochemical changes that can be detected optically. Therefore, it would be valuable to visualize the changes in both structure and biochemistry responses of organs for early detection of disease and monitoring of its progression. Oxidative stress is a biochemical byproduct of these diseases. Thus, obtaining sensitive and specific measurements of oxidative stress at the cellular level would provide vital information for understanding the pathogenesis of a disease. The objective of this research was to use a fluorescence optical imaging technique in order to evaluate the cellular redox state in kidney tissues, and develop an instrument to acquire high resolution 3D images of tissue. I have improved upon a custom-designed device called a cryoimager to acquire autofluorescent mitochondrial metabolic coenzyme (NADH, FAD) signals. The ratio of these fluorophores, referred to as the mitochondrial redox ratio (RR = NADH/ FAD), can be used as a quantitative metabolic marker of tissue. The improvement to the instrument includes addition of higher resolution imaging capabilities to the system. This improvement in the resolution of image acquisition enables microscopy imaging in cryo temperatures to obtain high resolution 3D images. The imaging is performed in cryogenic temperatures to increase the quantum yield of the fluorophores for a higher signal to noise ratio. I also implemented an automated tissue boundary detection algorithm. The algorithm will help provide more accurate results by removing the background of low contrast images. I examined the redox states of kidneys from genetically modified salt sensitive rats (SSBN13, SSp67phx -/-, and SSNox4-/-), in order to study the contribution of chromosome 13, the p67phox gene and the Nox4 gene in the development of salt sensitive hypertension. The result showed that the genetically manipulated rats are more resistant to hypertension caused by excess dietary salt ,, in comparison with salt sensitive (SS) rats. I also studied how endoglin genes affected the redox state and vascular networks of mice kidneys using high resolution images. The results showed that the next generation of the cryoimager can simultaneously monitor the structural changes and physiological state of tissue to quantify the effect of injuries. In conclusion, the combination of high resolution optical instrumentation and image processing tools provides quantitative physiological and structural information of diseased tissue due to oxidative stress

Machine Learning Approaches for Automated Glaucoma Detection using Clinical Data and Optical Coherence Tomography Images

Glaucoma is a multi-factorial, progressive blinding optic-neuropathy. A variety of factors, including genetics, vasculature, anatomy, and immune factors, are involved. Worldwide more than 80 million people are affected by glaucoma, and around 300,000 in Australia, where 50% remain undiagnosed. Untreated glaucoma can lead to blindness. Early detection by Artificial intelligence (AI) is crucial to accelerate the diagnosis process and can prevent further vision loss. Many proposed AI systems have shown promising performance for automated glaucoma detection using two-dimensional (2D) data. However, only a few studies had optimistic outcomes for glaucoma detection and staging. Moreover, the automated AI system still faces challenges in diagnosing at the clinicians’ level due to the lack of interpretability of the ML algorithms and integration of multiple clinical data. AI technology would be welcomed by doctors and patients if the "black box" notion is overcome by developing an explainable, transparent AI system with similar pathological markers used by clinicians as the sign of early detection and progression of glaucomatous damage. Therefore, the thesis aimed to develop a comprehensive AI model to detect and stage glaucoma by incorporating a variety of clinical data and utilising advanced data analysis and machine learning (ML) techniques. The research first focuses on optimising glaucoma diagnostic features by combining structural, functional, demographic, risk factor, and optical coherence tomography (OCT) features. The significant features were evaluated using statistical analysis and trained in ML algorithms to observe the detection performance. Three crucial structural ONH OCT features: cross-sectional 2D radial B-scan, 3D vascular angiography and temporal-superior-nasal-inferior-temporal (TSNIT) B-scan, were analysed and trained in explainable deep learning (DL) models for automated glaucoma prediction. The explanation behind the decision making of DL models were successfully demonstrated using the feature visualisation. The structural features or distinguished affected regions of TSNIT OCT scans were precisely localised for glaucoma patients. This is consistent with the concept of explainable DL, which refers to the idea of making the decision-making processes of DL models transparent and interpretable to humans. However, artifacts and speckle noise often result in misinterpretation of the TSNIT OCT scans. This research also developed an automated DL model to remove the artifacts and noise from the OCT scans, facilitating error-free retinal layers segmentation, accurate tissue thickness estimation and image interpretation. Moreover, to monitor and grade glaucoma severity, the visual field (VF) test is commonly followed by clinicians for treatment and management. Therefore, this research uses the functional features extracted from VF images to train ML algorithms for staging glaucoma from early to advanced/severe stages. Finally, the selected significant features were used to design and develop a comprehensive AI model to detect and grade glaucoma stages based on the data quantity and availability. In the first stage, a DL model was trained with TSNIT OCT scans, and its output was combined with significant structural and functional features and trained in ML models. The best-performed ML model achieved an area under the curve (AUC): 0.98, an accuracy of 97.2%, a sensitivity of 97.9%, and a specificity of 96.4% for detecting glaucoma. The model achieved an overall accuracy of 90.7% and an F1 score of 84.0% for classifying normal, early, moderate, and advanced-stage glaucoma. In conclusion, this thesis developed and proposed a comprehensive, evidence-based AI model that will solve the screening problem for large populations and relieve experts from manually analysing a slew of patient data and associated misinterpretation problems. Moreover, this thesis demonstrated three structural OCT features that could be added as excellent diagnostic markers for precise glaucoma diagnosis

Facial age synthesis using sparse partial least squares (the case of Ben Needham)

YesAutomatic facial age progression (AFAP) has been an active area of research in recent years. This is due to its numerous applications which include searching for missing. This study presents a new method of AFAP. Here, we use an Active Appearance Model (AAM) to extract facial features from available images. An ageing function is then modelled using Sparse Partial Least Squares Regression (sPLS). Thereafter, the ageing function is used to render new faces at different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a database of 500 face images with known ages. Furthermore, the algorithm is used to progress Ben Needham’s facial image that was taken when he was 21 months old to the ages of 6, 14 and 22 years. The algorithm presented in this paper could potentially be used to enhance the search for missing people worldwide

Imaging of Demyelination, Repair and Remyelination in Multiple Sclerosis

Multiple Sclerosis (MS) is characterised pathologically by both inflammatory demyelination and neurodegenerative neuroaxonal loss, occurring in varying degrees in the white matter (WM) and in the grey matter (GM). Studies of MS commonly use imaging surrogates of inflammation (e.g. MRI lesions) and neurodegeneration (e.g. atrophy) as outcome measures to assess potential neuroprotective effects. As trials of potentially remyelinating agents become more important in the spectrum of MS research, imaging outcomes sensitive to myelin, such are magnetisation transfer ratio (MTR), are required to adequately assess any such agents. With the above in mind, for this thesis, I performed 4 studies: 1. MTR and atrophy localisation in the GM using voxel-based morphometry - MRI measures of GM MTR and volume were used to assess the regional localisation of reduced MTR (in part reflecting demyelination) and atrophy (in part reflecting neuro-axonal loss) in 98 patients with MS, as well as 29 controls. Subgroups of MS patients were compared with controls, adjusting for age and gender. Overall, whilst some regionally consistent reductions in MTR and atrophy were seen in GM, this study found that these mostly do not co-localise. The differing location and extent of regional MTR and volumetric abnormalities in MS subgroups argues against a single mechanism for demyelination and neuronal loss in the GM of MS patients. 2. MRI substudy of Dronabinol (Δ⁹-THC) vs placebo – 273 patients with secondary progressive MS (SPMS) received either Dronabinol or placebo (in a ratio of 2:1), with the aim of assessing the potential neuroprotective effects of Dronobinol. T2-weighed (T2w) and T1-weighted (T1w) lesions, and percentage brain volume change (PBVC) were assessed over 3 years. Over the course of the entire study, the occurrence of new or enlarging T2w or T1w lesions, or PBVC was not affected by Dronabinol. 3. Individual lesion area MTR analysis of autologous mesenchymal stem cells (AMSC) in patients with SPMS – A proof-of-concept individual lesion area MTR analysis pathway was developed and used post-hoc on 10 patients with SPMS and optic nerve disease from the MSCIMS study, which investigated the potential reparative effects of AMSC. For T2w lesion areas, a significant difference in rate of change of MTR was noted after infusion; this was not seen with T1w lesion areas. 4. Individual lesion MTR analysis in a crossover study of AMSC in patients with active MS – the proof-of-concept work above was refined for use in STREAMS, a crossover study of AMSC. 12 patients with active MS received either AMSC or placebo for 24 weeks, and then crossover to the other arm for a further 24 weeks. MTR was measured at week 0, 12, 24, 26, and 48 in both old and newly appearing lesions. There was not noted to be any significant effect of AMSC on the MTR of either old or newly appearing lesions