XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions

Chronological age and biological sex estimation are two key tasks in a variety of procedures, including human identification and migration control. Issues such as these have led to the development of both semiautomatic and automatic prediction models, but the former are expensive in terms of time and human resources, while the latter lack the interpretability required to be applicable in real-life scenarios. This paper therefore proposes a new, fully automatic methodology for the estimation of age and sex. This first applies a tooth detection by means of a modified CNN with the objective of extracting the oriented bounding boxes of each tooth. Then, it feeds the image features inside the tooth boxes into a second CNN module designed to produce per-tooth age and sex probability distributions. The method then adopts an uncertainty-aware policy to aggregate these estimated distributions. Our approach yielded a lower mean absolute error than any other previously described, at 0.97 years. The accuracy of the sex classification was 91.82%, confirming the suitability of the teeth for this purpose. The proposed model also allows analyses of age and sex estimations on every tooth, enabling experts to identify the most relevant for each task or population cohort or to detect potential developmental problems. In conclusion, the performance of the method in both age and sex predictions is excellent and has a high degree of interpretability, making it suitable for use in a wide range of application scenariosS

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Transient Study of the Wetting Films in Porous Media Using 3D X-Ray Computed Micro-Tomography: Effect of Imbibition Rate and Pore Geometry

Imbibition in porous media is governed by the complex interplay between viscous and capillary forces, pore structure and fluid properties. Understanding and predicting imbibition is important in many natural and engineered applications; it affects the efficiency of oil production operations, the moisture and contaminant transport in soil science, and the formation of defects in certain types of composite materials. Majority of the studies published on the transient imbibition behavior in a porous medium were conducted in the simplified 2D transparent micromodels or the 2D projection visualization (X-ray or visible light) of the 3D porous medium. However, the pore level transient imbibition studies have not been reported on real three dimensional porous medium. The main challenge arises from the slowness of the present 3D imaging techniques in comparison with the speed of the pore filling events. To overcome these difficulties, we have developed a novel experimental technique using UV-induced polymerization, which allows the fluid phase distributions to be frozen in place during transient imbibition. Pore-scale structure of the front can then be examined in the 3D microscopic details using the X-ray Computed micro-Tomography (XCT). We have also developed a suite of advanced image segmentation programs to segment the grayscale XCT data. Image-based physically representative pore network generation techniques were unitized to quantify the geometry and topology of pore, wetting and nonwetting phase structure. Using UV initiated polymerization technique and image-based quantitative analysis tools; we have studied the effects of capillary number, pore structure and surface roughness on the structure of the transient imbibition front

ADVANCED INTRAOPERATIVE IMAGE REGISTRATION FOR PLANNING AND GUIDANCE OF ROBOT-ASSISTED SURGERY

Robot-assisted surgery offers improved accuracy, precision, safety, and workflow for a variety of surgical procedures spanning different surgical contexts (e.g., neurosurgery, pulmonary interventions, orthopaedics). These systems can assist with implant placement, drilling, bone resection, and biopsy while reducing human errors (e.g., hand tremors and limited dexterity) and easing the workflow of such tasks. Furthermore, such systems can reduce radiation dose to the clinician in fluoroscopically-guided procedures since many robots can perform their task in the imaging field-of-view (FOV) without the surgeon. Robot-assisted surgery requires (1) a preoperative plan defined relative to the patient that instructs the robot to perform a task, (2) intraoperative registration of the patient to transform the planning data into the intraoperative space, and (3) intraoperative registration of the robot to the patient to guide the robot to execute the plan. However, despite the operational improvements achieved using robot-assisted surgery, there are geometric inaccuracies and significant challenges to workflow associated with (1-3) that impact widespread adoption. This thesis aims to address these challenges by using image registration to plan and guide robot- assisted surgical (RAS) systems to encourage greater adoption of robotic-assistance across surgical contexts (in this work, spinal neurosurgery, pulmonary interventions, and orthopaedic trauma). The proposed methods will also be compatible with diverse imaging and robotic platforms (including low-cost systems) to improve the accessibility of RAS systems for a wide range of hospital and use settings. This dissertation advances important components of image-guided, robot-assisted surgery, including: (1) automatic target planning using statistical models and surgeon-specific atlases for application in spinal neurosurgery; (2) intraoperative registration and guidance of a robot to the planning data using 3D-2D image registration (i.e., an “image-guided robot”) for assisting pelvic orthopaedic trauma; (3) advanced methods for intraoperative registration of planning data in deformable anatomy for guiding pulmonary interventions; and (4) extension of image-guided robotics in a piecewise rigid, multi-body context in which the robot directly manipulates anatomy for assisting ankle orthopaedic trauma

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Using a water treatment residual and compost co-amendment as a sustainable soil improvement technology to enhance flood holding capacity

The recycling of clean wastes, such as those from the treatment of drinking water, has gained importance on the environmental agenda due to rising costs of landfill disposal and movement towards a ‘zero’ waste economy. More than one third of the globe’s soils are degraded and as such policies towards determining soil health parameters and reversing destruction of the globe’s most valuable non-renewable source are at the forefront of environmental debate. This thesis questions the opportunity for water treatment residual (WTR) to be used as a beneficial material for the co-amendment of soil with compost to improve the soil’s flood holding capacity (Kerr et al., 2016), which includes functions such as the water holding capacity, hydraulic conductivity, soil structure and shear strength. Currently, water treatment residual is typically sent to landfill for disposal, but this research shows that the reuse of WTR as a co-amendment is able to improve the flood holding capacity of soils. This research crosses the boundary between geotechnical and geoenvironmental and provides a holistic approach to quantifying a soil from both perspectives. Iron based water treatment residual from Northumbrian Water Ltd was used in both laboratory and field trials to establish the effect of single WTR and a compost and WTR co-amendment on the water holding capacity (the gravimetric water content, volumetric water content, volume change of samples i.e. swelling and shrinkage), and the effect of amendment on the erosional resistance, hydraulic conductivity and shear strength compared to a control soil. A series of four trials were conducted to develop and establish a novel method to determine the water holding capacity, supplemented by standard geotechnical methods to determine the flood holding capacity. The use of x-ray computed tomography has provided accompanying information on the morphology of dried WTR and changes in the internal characteristics of amended soil between a dry and wet state. The amendment application rate ranges from 10 – 50%. Experiments have shown that the single amendment of WTR, compared to a control soil, yields significant increases in the hydraulic conductivity (by up to a factor of 28), increases the shear strength of soils at low testing pressure (25 kPa) by 129%, increases the maximum gravimetric water content by up to 13.7%, and improves swelling by up to 12% (but only at the highest amendment rate, 30%), increases the maximum void ratio when saturated by 11%, and reduces shrinkage by maintaining porosity by 14%. However the application of WTR as a single amendment has implications for the chemical health of the soil as it is highly effective at immobilising phosphorous as and such cannot not effectively be used as a soil amendment. The single application of compost yielded significant improvement in the water holding capacity (improving gravimetric water content by up to 34.7%, increasing the sample volume by up to 83.3%, and increased the void ratio by 8.2%), however this application reduces the hydraulic conductivity by up to 84.5% and the shear strength by 3% compared to the control soil. Co-amendment using compost and WTR (in two forms, air dried 80% solids and wet at 20% solids, as produced from water treatment works) improved the flood holding capacity of soils by retaining the structural improvements of amendment using WTR and the water holding capacity improvements of compost. Compared to the control soil, for co-amended soils the gravimetric water content was improved by up to 25%, the volume increased by up to 51.7%, experienced 13% less shrinkage and an 11.5% increase in maximum void ratio. The hydraulic conductivity was also improved by up to 475%, and shear strength was increased at both low and high testing pressures by to 53.8%. Taking into account these effects of co-amendment on essential soil functions that determines a soil’s flood holding capacity (maximum gravimetric water content, volume change, resistance against shrinkage, void ratio (porosity), hydraulic conductivity and shear strength), the economical and environmental sustainability issues, the co-amendment of soil using compost and WTR may provide a solution to both recycling clean waste product and improving the quality of soil