Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage and Fractal Dimension. A constant Region of Interest (ROI) was defined and background extraction by a Gaussian Blurring Filter was performed. Image subtraction within ROI was followed by the sum of the RGB channels matrices. Percent Coverage was calculated on the resulting image. Fractal Dimension was estimated using the box-counting method. The images were then resized to a dimension in pixels equal to a power of 2, allowing subdivision into sub-multiple quadrants. In comparisons of manual and automated Percent Coverage and Fractal Dimension estimates, the former showed an overestimation tendency for both parameters. The primary limitations on the automatic analysis of benthic images were habitat variations in sediment texture and water column turbidity. The application of filters for background corrections is a required preliminary step for the efficient recognition of animals and bacterial mat patches

Eye Tracking Methods for Analysis of Visuo-Cognitive Behavior in Medical Imaging

Predictive modeling of human visual search behavior and the underlying metacognitive processes is now possible thanks to significant advances in bio-sensing device technology and machine intelligence. Eye tracking bio-sensors, for example, can measure psycho-physiological response through change events in configuration of the human eye. These events include positional changes such as visual fixation, saccadic movements, and scanpath, and non-positional changes such as blinks and pupil dilation and constriction. Using data from eye-tracking sensors, we can model human perception, cognitive processes, and responses to external stimuli. In this study, we investigated the visuo-cognitive behavior of clinicians during the diagnostic decision process for breast cancer screening under clinically equivalent experimental conditions involving multiple monitors and breast projection views. Using a head-mounted eye tracking device and a customized user interface, we recorded eye change events and diagnostic decisions from 10 clinicians (three breast-imaging radiologists and seven Radiology residents) for a corpus of 100 screening mammograms (comprising cases of varied pathology and breast parenchyma density). We proposed novel features and gaze analysis techniques, which help to encode discriminative pattern changes in positional and non-positional measures of eye events. These changes were shown to correlate with individual image readers' identity and experience level, mammographic case pathology and breast parenchyma density, and diagnostic decision. Furthermore, our results suggest that a combination of machine intelligence and bio-sensing modalities can provide adequate predictive capability for the characterization of a mammographic case and image readers diagnostic performance. Lastly, features characterizing eye movements can be utilized for biometric identification purposes. These findings are impactful in real-time performance monitoring and personalized intelligent training and evaluation systems in screening mammography. Further, the developed algorithms are applicable in other application domains involving high-risk visual tasks

A novel image analysis approach to characterise the effects of dietary components on intestinal morphology and immune system in Atlantic salmon

The intestinal tract of salmonids provides a dynamic interface that not only mediates nutrient uptake but also functions as the first line of defence against ingested pathogens. Exposure of the immune system to beneficial microorganisms and different dietary immunostimulants via the intestine has been shown to prime the immune system and help in the development of immune competence. Furthermore, the morphology and function of teleostean intestines are known to respond to feed components and to ingested and resident bacterial communities. Histological appraisal is still generally considered to be the gold standard for sensitive assessment of the effects of such dietary modulation. The aim of the present study was to improve understanding of salmonid intestinal function, structure and dynamics and to use the knowledge gained to develop a model for analysis, which would allow intestinal health to be assessed with respect to different intestinal communities and feed components. Virtual histology, the process of assessing digital images of histological slides, is gaining momentum as an approach to supplement traditional histological evaluation methodologies and at the same time, image analysis of digitised histological sections provides a practical means for quantifiable assessment of structural and functional changes in tissues, being both objective and reproducible. This project focused on the development of a rapid, practical analytical methodology based on advanced image analysis, that was able to measure and characterise a range of features of the intestinal histology of Atlantic salmon in a quantitative manner. In the first research chapter, the development of a novel histological assessment system based upon advanced image analysis was described, this being developed with the help of a soybean feed model known to induce enteropathy in Atlantic salmon. This tool targeted the evaluation of the extent of morphological changes occurring in the distal intestine of Atlantic salmon following dietary modulation. The final analytical methodology arrived at, could be conducted with minimal user-interaction, allowing rapid and objective assessment of 12 continuous variables per histological frame analysed. The processing time required for each histological frame was roughly 20-25 min, which greatly improved the efficiency of conducting such a quantitative assessment with respect to the time taken for a subjective semi-quantitative alternative approach. Significant agreement between the fully automated and the manual morphometric image segmentation was achieved, however, the strength of this quantitative approach was enhanced by the employment of interactive procedures, which enabled the operator / observer to rectify preceding automated segmentation steps, and account for the specimen’s variations. Results indicated that image analysis provided a viable alternative to a pathologist’s manual scoring, being more practical and time-efficient. In the second research chapter, feeding Atlantic salmon a high inclusion level of unrefined SBM (25 %) produced an inflammatory response in the distal intestine as previously described by other authors. The model feed trial successfully generated differentiable states, although these were not, for the most part, systemically differentiable through the majority of standard immunological procedures used, being only detectable morphologically. Quantitation of morphometric parameters associated with histological sections using the newly developed image analysis tool successfully allowed identification of major morphological changes. Image analysis was thus shown to provide a powerful tool for describing the histomorphological structure of Atlantic salmon distal intestine. In turn, the semi-automated image analysis methods were able to distinguish normal intestinal mucosa from those affected by enteritis. While individual parameters were less discriminatory, use of multivariate techniques allowed better discrimination of states and is likely to prove the most productive approach in further studies. Work described in the third research chapter sought to validate the semi-automated image analysis system to establish that it was measuring the parameters it was purported to be measuring, and to provide reassurance that it could reliably measure pre-determined features. This study, using the same sections for semi-quantitative and quantitative analyses, demonstrated that the quantitative indices performed well when compared to analogous semi-quantitative descriptive parameters of assessment for enteritis prognosis. The excellent reproducibility and accuracy performance levels indicated that the image analysis system was a useful and reliable morphometric method for the quantification of SB-induced enteritis in salmon. Other characteristics such as rapidity, simplicity and adaptability favour this method for image analysis, and are particularly useful where less experienced interpreters are performing the analysis. The work described in the fourth research chapter characterised changes in the morphology of the intestinal epithelial cells occurring as a result of dietary modulation and aspects of inflammatory infiltration, using a selected panel of enzyme and IHC markers. To accomplish this, image analysis techniques were used to evaluate and systematically optimise a quantitative immunolabelling assessment protocol. Digital computer-assisted quantification of labelling for cell proliferation and regeneration; programmed cell death or apoptosis; EGCs and t-cell like infiltrates; mobilisation of stress-related protein regenerative processes and facilitation of nutrient uptake and ion transport provided encouraging results. Through the description of the intestinal cellular responses at a molecular level, such IHC expression profiling further characterised the inflammatory reaction generated by the enteropathic diet. In addition, a number of potential diagnostic parameters were described for fish intestinal health e.g. the relative levels of antigenicity and the spatial distribution of antigens in tissues. Work described in the final research chapter focused on detailed characterisation of intestinal MCs / EGCs in order to try to elucidate their functional role in the intestinal immune responses. Through an understanding of their distribution, composition and ultrastructure, the intention was to better characterise these cells and their functional properties. The general morphology, histochemical characteristics and tissue distribution of these cells were explored in detail using histochemical, IHC and immunogold staining / labelling, visualised using light, confocal and TEM microscopy. Despite these extensive investigations, their physiological function and the content of their granules still remain somewhat obscure, although a role as immunodulatory cells reacting to various exogeneous signals through a finely regulated process and comparable to that causing the degranulation of mammalian MCs is suggested. The histochemical staining properties demonstrated for salmonid MCs / EGCs seem to resemble those of mammalian mucosal mast cells, with both acidophilic and basophilic components in their granules, and a granule content containing neuromodulator / neurotransmitter-peptides such as serotonin, met-enkephalin and substance-p. Consequently, distinguishable bio-chromogenic markers have been identified that are of utility in generating a discriminatory profile for image analysis of such cells

Microglial morphometric analysis: so many options, so little consistency

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist’s toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community

Microglial morphometric analysis: so many options, so little consistency

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist’s toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

Simple SummaryMonitoring the welfare of cattle and sheep in large pastures can be time-consuming, especially if the animals are scattered over large areas in semi-natural pastures. There are several technologies for monitoring animals with wearable or remote equipment for recording physiological or behavioural parameters and trigger alarms when the acquired information deviates from the normal. Automatic equipment allows continuous monitoring and may give more information than manual monitoring. Ear tags with electronic identification can detect visits to specific points. Collars with positioning (GPS) units can assess the animals' movements and habitat selection and, to some extent, their health and welfare. Digitally determined virtual fences, instead of the traditional physical ones, have the potential to keep livestock within a predefined area using audio signals in combination with weak electric shocks, although some individuals may have difficulties in responding as intended, potentially resulting in reduced animal welfare. Remote technology such as drones equipped with cameras can be used to count animals, determine their position and study their behaviour. Drones can also herd and move animals. However, the knowledge of the potential effects on animal welfare of digital technology for monitoring and managing grazing livestock is limited, especially regarding drones and virtual fences.The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences

Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese