Bayesian image restoration and bacteria detection in optical endomicroscopy

Optical microscopy systems can be used to obtain high-resolution microscopic images of tissue cultures and ex vivo tissue samples. This imaging technique can be translated for in vivo, in situ applications by using optical fibres and miniature optics. Fibred optical endomicroscopy (OEM) can enable optical biopsy in organs inaccessible by any other imaging systems, and hence can provide rapid and accurate diagnosis in a short time. The raw data the system produce is difficult to interpret as it is modulated by a fibre bundle pattern, producing what is called the “honeycomb effect”. Moreover, the data is further degraded due to the fibre core cross coupling problem. On the other hand, there is an unmet clinical need for automatic tools that can help the clinicians to detect fluorescently labelled bacteria in distal lung images. The aim of this thesis is to develop advanced image processing algorithms that can address the above mentioned problems. First, we provide a statistical model for the fibre core cross coupling problem and the sparse sampling by imaging fibre bundles (honeycomb artefact), which are formulated here as a restoration problem for the first time in the literature. We then introduce a non-linear interpolation method, based on Gaussian processes regression, in order to recover an interpretable scene from the deconvolved data. Second, we develop two bacteria detection algorithms, each of which provides different characteristics. The first approach considers joint formulation to the sparse coding and anomaly detection problems. The anomalies here are considered as candidate bacteria, which are annotated with the help of a trained clinician. Although this approach provides good detection performance and outperforms existing methods in the literature, the user has to carefully tune some crucial model parameters. Hence, we propose a more adaptive approach, for which a Bayesian framework is adopted. This approach not only outperforms the proposed supervised approach and existing methods in the literature but also provides computation time that competes with optimization-based methods

Human neutrophil elastase phenotyping: classifying neutrophils by function with novel imaging agents

This thesis describes approaches taken to classify the functionally diverse neutrophil by its different functions in a live-cell imaging tool. Human neutrophil elastase (HNE) cleaves over 30 substrates, its release is controlled with varying extents of degranulation and its activity is subject to complex modulation. HNE regulation is necessary for the maintenance of health via HNE’s multiplicity of functions. Although neutrophils are routinely quantified in assessments of chronic inflammation, we do not have a clinically translatable, integrated tool for measuring HNE in human tissues. Two novel probes have been characterised and multiplexed to form the basis of an imaging technique for understanding the functional implications of neutrophil activity in human tissues, in real-time. Neutrophil Activation Probe (NAP) and VE200 are novel probes for HNE activity and presence. These probes are characterised in vitro as sensitive and HNE-specific imaging agents for live-cell imaging and image cytometry. Multiplexed NAP and HNE are detect neutrophil activation, apoptosis and necrosis. These imaging agents can inform deep profiling techniques to separate neutrophils into untreated, primed, activated and primed-activated states and endogenous vs. exogenous stimulus sub-states. Finally, sections of adenocarcinomatous human lung and whole human lungs, ventilated ex vivo, demonstrate the applicability of HNEbased, multiparametric profiling and neutrophil activation detection to clinically relevant platforms