Abstract
Original language | English |
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Pages (from-to) | 18-31 |
Number of pages | 14 |
Journal | Medical Image Analysis |
Volume | 57 |
Early online date | 24 Jun 2019 |
DOIs | |
Publication status | Published - Oct 2019 |
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Keywords
- Bacteria detection
- Bayesian estimation
- Irregular spatial sampling
- Optical endomicroscopy
- Outlier detection
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging
- Computer Vision and Pattern Recognition
- Health Informatics
- Computer Graphics and Computer-Aided Design
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Bayesian Bacterial Detection Using Irregularly Sampled Optical Endomicroscopy Images. / Karam Mohammed Abdelkarim, Ahmed; Altmann, Yoann; Akram, Ahsan; McCool, Paul; Perperidis, Antonios; Dhaliwal, Kevin; McLaughlin, Stephen.
In: Medical Image Analysis, Vol. 57, 10.2019, p. 18-31.Research output: Contribution to journal › Article
TY - JOUR
T1 - Bayesian Bacterial Detection Using Irregularly Sampled Optical Endomicroscopy Images
AU - Karam Mohammed Abdelkarim, Ahmed
AU - Altmann, Yoann
AU - Akram, Ahsan
AU - McCool, Paul
AU - Perperidis, Antonios
AU - Dhaliwal, Kevin
AU - McLaughlin, Stephen
PY - 2019/10
Y1 - 2019/10
N2 - Pneumonia is a major cause of morbidity and mortality of patients in intensive care. Rapid determination of the presence and gram status of the pathogenic bacteria in the distal lung may enable a more tailored treatment regime. Optical Endomicroscopy (OEM) is an emerging medical imaging platform with preclinical and clinical utility. Pulmonary OEM via multi-core fibre bundles has the potential to provide in vivo, in situ, fluorescent molecular signatures of the causes of infection and inflammation. This paper presents a Bayesian approach for bacterial detection in OEM images. The model considered assumes that the observed pixel fluorescence is a linear combination of the actual intensity value associated with tissues or background, corrupted by additive Gaussian noise and potentially by an additional sparse outlier term modelling anomalies (bacteria). The bacteria detection problem is formulated in a Bayesian framework and prior distributions are assigned to the unknown model parameters. A Markov chain Monte Carlo algorithm based on a partially collapsed Gibbs sampler is used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic datasets for which good performance is obtained. Analysis is then conducted using two ex vivo lung datasets in which fluorescently labelled bacteria are present in the distal lung. A good correlation between bacteria counts identified by a trained clinician and those of the proposed method, which detects most of the manually annotated regions, is observed.
AB - Pneumonia is a major cause of morbidity and mortality of patients in intensive care. Rapid determination of the presence and gram status of the pathogenic bacteria in the distal lung may enable a more tailored treatment regime. Optical Endomicroscopy (OEM) is an emerging medical imaging platform with preclinical and clinical utility. Pulmonary OEM via multi-core fibre bundles has the potential to provide in vivo, in situ, fluorescent molecular signatures of the causes of infection and inflammation. This paper presents a Bayesian approach for bacterial detection in OEM images. The model considered assumes that the observed pixel fluorescence is a linear combination of the actual intensity value associated with tissues or background, corrupted by additive Gaussian noise and potentially by an additional sparse outlier term modelling anomalies (bacteria). The bacteria detection problem is formulated in a Bayesian framework and prior distributions are assigned to the unknown model parameters. A Markov chain Monte Carlo algorithm based on a partially collapsed Gibbs sampler is used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic datasets for which good performance is obtained. Analysis is then conducted using two ex vivo lung datasets in which fluorescently labelled bacteria are present in the distal lung. A good correlation between bacteria counts identified by a trained clinician and those of the proposed method, which detects most of the manually annotated regions, is observed.
KW - Bacteria detection
KW - Bayesian estimation
KW - Irregular spatial sampling
KW - Optical endomicroscopy
KW - Outlier detection
UR - http://www.scopus.com/inward/record.url?scp=85067949911&partnerID=8YFLogxK
U2 - 10.1016/j.media.2019.06.009
DO - 10.1016/j.media.2019.06.009
M3 - Article
VL - 57
SP - 18
EP - 31
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
ER -