A Software Retina for Egocentric & Robotic Vision Applications on Mobile Platforms

We present work in progress to develop a low-cost highly integrated camera sensor for egocentric and robotic vision. Our underlying approach is to address current limitations to image analysis by Deep Convolutional Neural Networks, such as the requirement to learn simple scale and rotation transformations, which contribute to the large computational demands for training and opaqueness of the learned structure, by applying structural constraints based on known properties of the human visual system. We propose to apply a version of the retino-cortical transform to reduce the dimensionality of the input image space by a factor of ex100, and map this spatially to transform rotations and scale changes into spatial shifts. By reducing the input image size accordingly, and therefore learning requirements, we aim to develop compact and lightweight egocentric and robot vision sensor using a smartphone as the target platfor

A Biologically Motivated Software Retina for Robotic Sensors Based on Smartphone Technology

A key issue in designing robotics systems is the cost of an integrated camera sensor that meets the bandwidth/processing requirement for many advanced robotics applications, especially lightweight robotics applications, such as visual surveillance or SLAM in autonomous aerial vehicles. There is currently much work going on to adapt smartphones to provide complete robot vision systems, as the phone is so exquisitely integrated having camera(s), inertial sensing, sound I/O and excellent wireless connectivity. Mass market production makes this a very low-cost platform and manufacturers from quadrotor drone suppliers to children’s toys, such as the Meccanoid robot, employ a smartphone to provide a vision system/control system. Accordingly, many research groups are attempting to optimise image analysis, computer vision and machine learning libraries for the smartphone platform. However current approaches to robot vision remain highly demanding for mobile processors such as the ARM, and while a number of algorithms have been developed, these are very stripped down, i.e. highly compromised in function or performance For example, the semi-dense visual odometry implementation of [1] operates on images of only 320x240pixels. In our research we have been developing biologically motivated foveated vision algorithms, potentially some 100 times more efficient than their conventional counterparts, based on a model of the mammalian retina we have developed. Vision systems based on the foveated architectures found in mammals have the potential to reduce bandwidth and processing requirements by about x100 - it has been estimated that our brains would weigh ~60Kg if we were to process all our visual input at uniform high resolution. We have reported a foveated visual architecture that implements a functional model of the retina-visual cortex to produce feature vectors that can be matched/classified using conventional methods, or indeed could be adapted to employ Deep Convolutional Neural Nets for the classification/interpretation stage, [2,3,4]. We are now at the early stages of investigating how best to port our foveated architecture onto a smartphone platform. To achieve the required levels of performance we propose to optimise our retina model to the ARM processors utilised in smartphones, in conjunction with their integrated GPUs, to provide a foveated smart vision system on a smartphone. Our current goal is to have a foveated system running in real-time to serve as a front-end robot sensor for tasks such as general purpose object recognition and reliable dense SLAM using a commercial off-the-shelf smartphone which communicates with conventional hardware performing back-end visual classification/interpretation. We believe that, as in Nature, space-variance is the key to achieving the necessary data reduction to be able to implement the complete visual processing chain on the smartphone itself

A Biologically Motivated Software Retina for Robotic Sensors for ARM-Based Mobile Platform Technology

A key issue in designing robotics systems is the cost of an integrated camera sensor that meets the bandwidth/processing requirement for many advanced robotics applications, especially lightweight robotics applications, such as visual surveillance or SLAM in autonomous aerial vehicles. There is currently much work going on to adapt smartphones to provide complete robot vision systems, as the smartphone is so exquisitely integrated by having camera(s), inertial sensing, sound I/O and excellent wireless connectivity. Mass market production makes this a very low-cost platform and manufacturers from quadrotor drone suppliers to children’s toys, such as the Meccanoid robot [5], employ a smartphone to provide a vision system/control system [7,8]. Accordingly, many research groups are attempting to optimise image analysis, computer vision and machine learning libraries for the smartphone platform. However current approaches to robot vision remain highly demanding for mobile processors such as the ARM, and while a number of algorithms have been developed, these are very stripped down, i.e. highly compromised in function or performance. For example, the semi-dense visual odometry implementation of [1] operates on images of only 320x240pixels. In our research we have been developing biologically motivated foveated vision algorithms based on a model of the mammalian retina [2], potentially 100 times more efficient than their conventional counterparts. Accordingly, vision systems based on the foveated architectures found in mammals have also the potential to reduce bandwidth and processing requirements by about x100 - it has been estimated that our brains would weigh ~60Kg if we were to process all our visual input at uniform high resolution. We have reported a foveated visual architecture [2,3,4] that implements a functional model of the retina-visual cortex to produce feature vectors that can be matched/classified using conventional methods, or indeed could be adapted to employ Deep Convolutional Neural Nets for the classification/interpretation stage. Given the above processing/bandwidth limitations, a viable way forward would be to perform off-line learning and implement the forward recognition path on the mobile platform, returning simple object labels, or sparse hierarchical feature symbols, and gaze control commands to the host robot vision system and controller. We are now at the early stages of investigating how best to port our foveated architecture onto an ARM-based smartphone platform. To achieve the required levels of performance we propose to port and optimise our retina model to the mobile ARM processor architecture in conjunction with their integrated GPUs. We will then be in the position to provide a foveated smart vision system on a smartphone with the advantage of processing speed gains and bandwidth optimisations. Our approach will be to develop efficient parallelising compilers and perhaps propose new processor architectural features to support this approach to computer vision, e.g. efficient processing of hexagonally sampled foveated images. Our current goal is to have a foveated system running in real-time on at least a 1080p input video stream to serve as a front-end robot sensor for tasks such as general purpose object recognition and reliable dense SLAM using a commercial off-the-shelf smartphone. Initially this system would communicate a symbol stream to conventional hardware performing back-end visual classification/interpretation, although simple object detection and recognition tasks should be possible on-board the device. We propose that, as in Nature, foveated vision is the key to achieving the necessary data reduction to be able to implement complete visual recognition and learning processes on the smartphone itself

In Vivo Measurements of T2 Relaxation Time of Mouse Lungs during Inspiration and Expiration

Abstract Purpose The interest in measurements of magnetic resonance imaging relaxation times, T1, T2, T2*, with intention to characterize healthy and diseased lungs has increased recently. Animal studies play an important role in this context providing models for understanding and linking the measured relaxation time changes to the underlying physiology or disease. The aim of this work was to study how the measured transversal relaxation time (T2) in healthy lungs is affected by normal respiration in mouse. Method T2 of lung was measured in anaesthetized freely breathing mice. Image acquisition was performed on a 4.7 T, Bruker BioSpec with a multi spin-echo sequence (Car-Purcell-MeiboomGill) in both end-expiration and end-inspiration. The echo trains consisted of ten echoes of inter echo time 3.5 ms or 4.0 ms. The proton density, T2 and noise floor were fitted to the measured signals of the lung parenchyma with a Levenberg-Marquardt least-squares threeparameter fit. Results T2 in the lungs was longer (p<0.01) at end-expiration (9.7±0.7 ms) than at end-inspiration (9.0±0.8 ms) measured with inter-echo time 3.5 ms. The corresponding relative proton density (lung/muscle tissue) was higher (p<0.001) during end-expiration, (0.61±0.06) than during end-inspiration (0.48±0.05). The ratio of relative proton density at end-inspiration to that at end-expiration was 0.78±0.09. Similar results were found for inter-echo time 4.0 ms and there was no significant difference between the T2 values or proton densities acquired with different interecho times. The T2 value increased linearly (p< 0.001) with proton density

Magnetic Resonance Imaging in Clinical Trials of Diabetic Kidney Disease

Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD

Clinical Characteristics of a Non-Alcoholic Fatty Liver Disease Population Across the Fibrosis Spectrum Measured by Magnetic Resonance Elastography: Analysis of Screening Data

Introduction: Non-alcoholic fatty liver disease (NAFLD), one of the most common liver diseases, is associated with liver-related complications and metabolic comorbidities. The phenotype is wide, ranging from simple steatosis to non-alcoholic steatohepatitis with advanced fibrosis. In this analysis of a phase 1 trial, clinical characteristics of screened subjects with NAFLD were studied according to the extent of fibrosis assessed using magnetic resonance elastography (MRE). Methods: One hundred ninety-four subjects with body mass index (BMI) of 25–40\ua0kg/m2 and suspected NAFLD were assessed by MRE and grouped by MRE thresholds as a proxy for fibrosis staging (groups 0–4). Data were summarized by group levels, and correlation analyses between MRE values and clinical parameters (including magnetic resonance imaging-proton density fat fraction) were performed. Results: Most subjects had MRE values in the lower range (groups 0–1; N = 148). Type 2 diabetes (T2D) and BMI > 35\ua0kg/m2 were more frequent in groups with higher than lower MRE values. Subjects in the highest MRE groups also tended to be older and have higher liver enzyme concentrations compared with lower MRE groups. No, or weak, correlations were found between MRE values and clinical parameters (all r values ≤ 0.45). Conclusions: There was considerable variation and overlap in clinical characteristics across the spectrum of liver stiffness. Although groups with high MRE values generally included more subjects with T2D and obesity, and had higher age and concentrations of liver enzymes, the clinical characteristics did not strongly correlate with MRE scores in this population. Trial Registration: Registered on Clinicaltrials.gov on November 29, 2017 (NCT03357380)