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Bayesian bacterial detection using irregularly sampled optical endomicroscopy images.

Ahmed Karam Eldaly1, Yoann Altmann2, Ahsan Akram3

  • 1Electrical and Electronic Engineering Department, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom; MRC Centre for Inflammation Research, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, United Kingdom; Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt.

Medical Image Analysis
|July 2, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method for detecting bacteria in optical endomicroscopy images of the lungs. The approach accurately identifies bacterial presence, aiding in faster pneumonia diagnosis and treatment.

Keywords:
Bacteria detectionBayesian estimationIrregular spatial samplingOptical endomicroscopyOutlier detection

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Infectious Disease Research

Background:

  • Pneumonia significantly impacts intensive care unit patients, necessitating rapid diagnostics.
  • Optical Endomicroscopy (OEM) offers in vivo imaging for detecting infection causes.
  • Tailored treatment for pneumonia requires timely identification of pathogenic bacteria and their Gram status.

Purpose of the Study:

  • To develop and validate a Bayesian approach for bacterial detection in pulmonary Optical Endomicroscopy (OEM) images.
  • To enable rapid, in situ identification of bacteria in the distal lung for improved pneumonia management.

Main Methods:

  • A Bayesian framework was employed for bacterial detection in OEM images.
  • A model was developed assuming pixel fluorescence is a linear combination of tissue intensity, Gaussian noise, and sparse outliers representing bacteria.
  • A Markov chain Monte Carlo algorithm, specifically a partially collapsed Gibbs sampler, was used to analyze posterior distributions.

Main Results:

  • The proposed Bayesian algorithm demonstrated good performance in simulations using synthetic datasets.
  • Analysis of ex vivo lung datasets with fluorescently labeled bacteria showed a strong correlation between clinician counts and algorithm detection.
  • The method successfully identified most manually annotated regions containing bacteria.

Conclusions:

  • The Bayesian approach provides a robust method for bacterial detection in pulmonary OEM images.
  • This technique has the potential to significantly improve the speed and accuracy of pneumonia diagnosis in critical care settings.
  • Further development could lead to enhanced, real-time diagnostic tools for lung infections.