Estimating Bacterial and Cellular Load in FCFM Imaging

Abstract

Abstract:We address the task of estimating bacterial and cellular load in the human distal lung with fibered confocal fluorescence microscopy (FCFM). In pulmonary FCFM some cells can display autofluorescence, and they appear as disc like objects in the FCFM images, whereas bacteria, although not autofluorescent, appear as bright blinking dots when exposed to a targeted smartprobe. Estimating bacterial and cellular load becomes a challenging task due to the presence of background from autofluorescent human lung tissues, i.e., elastin, and imaging artifacts from motion etc. We create a database of annotated images for both these tasks where bacteria and cells were annotated, and use these databases for supervised learning. We extract image patches around each pixel as features, and train a classifier to predict if a bacterium or cell is present at that pixel. We apply our approach on two datasets for detecting bacteria and cells respectively. For the bacteria dataset, we show that the estimated bacterial load increases after introducing the targeted smartprobe in the presence of bacteria. For the cell dataset, we show that the estimated cellular load agrees with a clinician's assessment.

Enhanced avidity from a multivalent fluorescent antimicrobial peptide enables pathogen detection in a human lung model

“…This approach shows particular promise for the future as it can be multiplexed using fiber based systems capable of multispectral imaging 24,25 , used in combination with other SmartProbes for Gram-negative bacteria 13 or inflammatory cells 26,27 and compounds can be delivered directly into the imaging field of view 28 . Furthermore, with ongoing refinements to the image analysis algorithms through multiple methods 29–31 , automated readouts of the signals generated may help decision making and advance our understanding of the pathophysiology during suspected pneumonia.…”

Image computing for fibre-bundle endomicroscopy: A review

“…Finally, in the pulmonary tract, (Namati et al, 2008) uncertainty in inference when limited amount of data or information is available. (Seth et al, 2017(Seth et al, , 2018 quantified bacterial and cellular load in the human lung adopting and adapting a learning-to-count (Arteta et al, 2014) approach, employing a multi-resolution, spatio-temporal template matching scheme using radial basis functions network.…”

“…The algorithm was an extension of for denoising along with outlier detection and removal in sparsely, irregularly sampled data. Such fully unsupervised approches offer a flexible and consistent methodology to deal with uncertainty in inference when limited amount of data or information is available (Seth et al, 2017(Seth et al, , 2018. quantified bacterial and cellular load in the human lung adopting and adapting a learning-to-count(Arteta et al, 2014) approach, employing a multi-resolution, spatio-temporal template matching scheme using radial basis functions network.Image understandingAnother component of the image computing pipeline is the higher-level understanding and exploitation of the acquired, reconstructed and sometimes processed data, in an attempt to extract clinically and biologically relevant information, and consequently guide the diagnostic process.…”

Fibre-based spectral ratio endomicroscopy for contrast enhancement of bacterial imaging and pulmonary autofluorescence

“…As an initial proof-of-concept, we demonstrated the potential of the system to detecting bacteria pre-labelled with SmartProbe (NBD-PMX) in lung tissue. This has been the subject of significant efforts in signal processing and image analyses [28] as well as other modalities such as fluorescence lifetime imaging [29]. Our new approach offers a simple, readily translatable and effective solution to the technical challenge.…”