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Advances in the CMOS process enables single-photon detection and timing to occur within an individual pixel. Pixel arrays incorporating this in-pixel timing methodology have been increasing in size, facilitating time of flight (ToF) imaging. This means that a fully parallel approach can be made to the collection of images, dramatically speeding up data collection in relation to sample scanning and allowing imaging based on time-correlated single-photon counting (TCSPC) to be made in real time. In this work we make use of a commercial TCSPC camera, based on an array of 192 × 126 imaging pixels with in-pixel timing applied in the area of time-domain near infrared spectroscopy. The potential of remote imaging using this methodology was demonstrated using two model systems. The effect of increasing scattering coefficient using a liquid based (milk added to water) system to assess real time potential was investigated. Additionally a more complex system consisting of a solid with liquid interior (egg) to verify the potential of remote imaging into the interior of the object with different illumination conditions was studied. Using a time-slicing approach intensity images related to each time bin in the ToF distribution were obtained and contrast between the yolk and albumin within the egg was observed.

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and image analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. First, we critically review existing literature in the CV domain that addresses complex vision tasks, including: medical image classification; shape and object recognition from images; and medical segmentation. Second, we present an in-depth discussion of the various challenges that are considered barriers to accelerating research, development, and deployment of intelligent CV methods in real-life medical applications and hospitals. Finally, we conclude by discussing future directions.

The presence of harmful algal bloom in many reservoirs around the world, alongside the lack of sanitation law/ordinance regarding cyanotoxin monitoring (particularly in developing countries), create a scenario in which the local population could potentially chronically consume cyanotoxin-contaminated waters. Therefore, it is crucial to develop low cost tools to detect possible systems failures and consequent toxin release inferred by morphological changes of cyanobacteria in the raw water. This paper aimed to look for the best combination of convolutional neural network (CNN), optimizer and image segmentation technique to differentiate P. agardhii trichomes before and after chemical stress caused by the addition of hydrogen peroxide. This method takes a step towards accurate monitoring of cyanobacteria in the field without the need for a mobile lab. After testing three different network architectures (AlexNet, 3ConvLayer and 2ConvLayer), four different optimizers (Adam, Adagrad, RMSProp and SDG) and five different image segmentations methods (Canny Edge Detection, Morphological Filter, HP filter, GrabCut and Watershed), the combination 2ConvLayer with Adam optimizer and GrabCut segmentation, provided the highest median accuracy (93.33%) for identifying H2O2-induced morphological changes in P. agardhii. Our results emphasize the fact that the trichome classification problem can be adequately tackled with a limited number of learned features due to the lack of complexity in micrographs from before and after chemical stress. To the authors' knowledge, this is the first time that CNNs were applied to detect morphological changes in cyanobacteria caused by chemical stress. Thus, it is a significant step forward in developing low cost tools based on image recognition, to shield water consumers, especially in the poorest regions, against cyanotoxin-contaminated water.

One of the key features of most document image digitisation systems is the capability of discerning between the main components of the printed representation at hand. In the case of engineering drawings, such as circuit diagrams, telephone exchanges or process diagrams, the three main shapes to be localised are the symbols, text and connectors. While most of the state of the art devotes to top-down recognition approaches which attempt to recognise these shapes based on their features and attributes, less work has been devoted to localising the actual pixels that constitute each shape, mostly because of the difficulty in obtaining a reliable source of training samples to classify each pixel individually. In this work, we present a convolutional neural network (CNN) capable of classifying each pixel, using a type of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID) as a case study. To obtain the training patches, we have used a semi-automated heuristics-based tool which is capable of accurately detecting and producing the symbol, text and connector layers of a particular P&ID standard in a considerable amount of time (given the need of human interaction). Experimental validation shows that the CNN is capable of obtaining these three layers in a reduced time, with the pixel window size used to generate the training samples having a strong influence on the recognition rate achieved for the different shapes. Furthermore, we compare the average run time that both the heuristics-tool and the CNN need in order to produce the three layers for a single diagram, indicating future directions to increase accuracy for the CNN without compromising the speed.

Objectives The aim of this study was to evaluate a feline coronavirus (FCoV) reverse transcriptase quantitative PCR (RT-qPCR) on fine-needle aspirates (FNAs) from mesenteric lymph nodes (MLNs) collected in sterile saline for the purpose of diagnosing non-effusive feline infectious peritonitis (FIP) in cats. Methods First, the ability of the assay to detect viral RNA in MLN FNA preparations compared with MLN biopsy preparations was assessed in matched samples from eight cats. Second, a panel of MLN FNA samples was collected from a series of cats representing non-effusive FIP cases (n = 20), FCoV-seropositive individuals (n = 8) and FCoV-seronegative individuals (n = 18). Disease status of the animals was determined using a combination of gross pathology, histopathology and/or ‘FIP profile’, consisting of serology, clinical pathology and clinical signs. Results Viral RNA was detected in 18/20 non-effusive FIP cases; it was not detected in two cases that presented with neurological FIP. Samples from 18 seronegative non-FIP control cats and 7/8 samples from seropositive non-FIP control cats contained no detectable viral RNA. Thus, as a method for diagnosing non-effusive FIP, MLN FNA RT-qPCR had an overall sensitivity of 90.0% and specificity of 96.1%. Conclusions and relevance In cases with a high index of suspicion of disease, RT-qPCR targeting FCoV in MLN FNA can provide important information to support the ante-mortem diagnosis of non-effusive FIP. Importantly, viral RNA can be reliably detected in MLN FNA samples in saline submitted via the national mail service. When applied in combination with biochemistry, haematology and serological tests in cases with a high index of suspicion of disease, the results of this assay may be used to support a diagnosis of non-effusive FIP.