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Bayesian Statistical Analysis for Bacterial Detection in Pulmonary Endomicroscopic Fluorescence Lifetime Imaging.

IEEE TRANSACTIONS ON IMAGE PROCESSING(2024)

Univ Edinburgh

Cited 2|Views23
Abstract
Pneumonia, a respiratory disease often caused by bacterial infection in the distal lung, requires rapid and accurate identification, especially in settings such as critical care. Initiating or de-escalating antimicrobials should ideally be guided by the quantification of pathogenic bacteria for effective treatment. Optical endomicroscopy is an emerging technology with the potential to expedite bacterial detection in the distal lung by enabling in vivo and in situ optical tissue characterisation. With advancements in detector technology, optical endomicroscopy can utilize fluorescence lifetime imaging (FLIM) to help detect events that were previously challenging or impossible to identify using fluorescence intensity imaging. In this paper, we propose an iterative Bayesian approach for bacterial detection in FLIM. We model the FLIM image as a linear combination of background intensity, Gaussian noise, and additive outliers (labelled bacteria). While previous bacteria detection methods model anomalous pixels as bacteria, here the FLIM outliers are modelled as circularly symmetric Gaussian-shaped objects, based on their discrete shape observed through visual analysis and the physical nature of the imaging modality. A Hierarchical Bayesian model is used to solve the bacterial detection problem where prior distributions are assigned to unknown parameters. A Metropolis-Hastings within Gibbs sampler draws samples from the posterior distribution. The proposed method's detection performance is initially measured using synthetic images, and shows significant improvement over existing approaches. Further analysis is conducted on real optical endomicroscopy FLIM images annotated by trained personnel. The experiments show the proposed approach outperforms existing methods by a margin of +16.85% ( F1 ) for detection accuracy.
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Key words
Microorganisms,Lung,Fluorescence,Imaging,Optical imaging,Endomicroscopy,Biomedical optical imaging,Bacteria detection,fluorescence lifetime imaging,optical endomicroscopy,Bayesian statistical analysis
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要点】:本文提出了一种迭代贝叶斯方法,用于荧光寿命成像中细菌的检测,提高了检测准确性,创新点在于将FLIM图像中的异常值建模为圆形对称高斯形状的对象。

方法】:采用层次贝叶斯模型对FLIM图像进行建模,将图像视为背景强度、高斯噪声和加性异常值(标记为细菌)的线性组合,并利用Metropolis-Hastings within Gibbs采样器从后验分布中抽取样本。

实验】:通过合成图像和真实光学内窥镜FLIM图像(由专业人员标注)进行实验,实验结果显示,该方法比现有方法在检测准确性上提高了16.85%的F1分数。