Bayesian bacterial detection using irregularly sampled optical endomicroscopy images

Eldaly, Ahmed Karam; Altmann, Yoann; Akram, Ahsan R.; McCool, Paul; Perperidis, Antonios; Dhaliwal, Kevin; McLaughlin, Stephen

doi:10.1016/j.media.2019.06.009

Cited by 7 publications

(18 citation statements)

References 31 publications

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“…Lung parenchyma Specific pCLE patterns of ILD; manually [50][51][52][53][54][55][56][57] and automatically [59] Biopsy guidance An increase of diagnostic yield and a decrease of the complication risk during TBCB Pneumonia Lung parenchyma Identification of pCLE features in pneumonia [62,[64][65][66][67][68][69][70][71][72] Rapid in situ detection of pathogens Study host-pathogen interactions in response to therapies…”

Section: Ildmentioning

confidence: 99%

“…To meet the needs of separating fluorescent targets during pCLE imaging, PARKER et al [70] designed a simple widefield ratiometric pCLE imaging platform with low cost in which the contrast between similar fluorescent signals could be enhanced and showed that this system was able to detect labelled Gram-negative bacteria even in the context of extremely autofluorescent lung tissue. Moreover, ELDALY et al [71] presented an algorithm based on a hierarchical Bayesian model without the limitation of a specific spatial organisation of the fibre bundle. It represented a fully automatic approach that helps detect labelled bacteria in pCLE imaging and distinguish controls from bacterial loads of different concentrations.…”

Section: Pneumoniamentioning

confidence: 99%

“…Moreover, E ldaly et al . [ 71 ] presented an algorithm based on a hierarchical Bayesian model without the limitation of a specific spatial organisation of the fibre bundle. It represented a fully automatic approach that helps detect labelled bacteria in pCLE imaging and distinguish controls from bacterial loads of different concentrations.…”

Section: Clinical Applicationsmentioning

confidence: 99%

“…In the clinic, manual analysis of CLE images is a very labour-intensive and time-consuming process, and the imaging interpretation has characteristics of subjectivity [ 71 , 95 ]. There has therefore been great interest in the automated analysis of CLE images, using machine learning tools, to enable accurate predictions with minimal human intervention [ 12 , 18 , 59 , 71 ].…”

Section: Future Prospectsmentioning

confidence: 99%

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The role of confocal laser endomicroscopy in pulmonary medicine

Tian¹,

Huang²,

Zhang

et al. 2023

Eur Respir Rev

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Accurate diagnosis and subsequent therapeutic options in pulmonary diseases mainly rely on imaging methods and histological assessment. However, imaging examinations are hampered by the limited spatial resolution of images and most procedures that are related to histological assessment are invasive with associated complications. As a result, a high-resolution imaging technology – confocal laser endomicroscopy (CLE), which is at the forefront and enables real-time microscopic visualisation of the morphologies and architectures of tissues or cells – has been developed to resolve the clinical dilemma pertaining to current techniques. The current evidence has shown that CLE has the potential to facilitate advanced diagnostic capabilities, to monitor and to aid the tailored treatment regime for patients with pulmonary diseases, as well as to expand the horizon for unravelling the mechanism and therapeutic targets of pulmonary diseases. In the future, if CLE can be combined with artificial intelligence, early, rapid and accurate diagnosis will be achieved through identifying the images automatically. As promising as this technique may be, further investigations are required before it can enter routine clinical practice.

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Section: Ildmentioning

confidence: 99%

Section: Pneumoniamentioning

confidence: 99%

Section: Clinical Applicationsmentioning

confidence: 99%

Section: Future Prospectsmentioning

confidence: 99%

See 2 more Smart Citations

The role of confocal laser endomicroscopy in pulmonary medicine

Tian¹,

Huang²,

Zhang

et al. 2023

Eur Respir Rev

View full text Add to dashboard Cite

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“…In practice, strong correlations appear between z and t. Moreover, as z can be sparse, sampling f (t, s 2 |y, ∆ \t , Φ \(s 2 ) ), where H \u denotes the parameter vector H whose parameter u is omitted, via Gibbs sampling results in very slow convergence. Hence, we use a partially collapsed Gibbs sampler (PCGS) which provides better mixing and convergence properties [28][29][30][31][32][33]. The PCGS used here samples groups of variables (e.g., (z, t)) from their joint posterior distribution rather than from their conditional distributions.…”

Section: Bayesian Inferencementioning

confidence: 99%

Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography

et al. 2021

Self Cite

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In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method, based on a split and augmented Gibbs sampler, is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator, whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns, in addition to being able to quantify their uncertainty measures, in comparison with existing methods.

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Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

Gouzou,

Taimori,

Haloubi

et al. 2023

Methods Appl. Fluoresc.

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Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks.Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources.In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics.However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm.In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

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Bayesian bacterial detection using irregularly sampled optical endomicroscopy images

Cited by 7 publications

References 31 publications

The role of confocal laser endomicroscopy in pulmonary medicine

The role of confocal laser endomicroscopy in pulmonary medicine

Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography

Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

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