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J. Imaging 2018, 4(1), 11;

Estimating Bacterial and Cellular Load in FCFM Imaging

School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
Pulmonary Molecular Imaging Group, MRC Center for Inflammation Research, Queens Medical Research Institute, University of Edinburgh, Edinburgh EH14 4TJ, UK
This paper is an extended version of our paper published in Annual Conference on Medical Image Understanding and Analysis, Edinburgh, UK, 11–13 July 2017.
Author to whom correspondence should be addressed.
Received: 7 November 2017 / Revised: 13 December 2017 / Accepted: 13 December 2017 / Published: 5 January 2018
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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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. View Full-Text
Keywords: FCFM imaging; lung; bacteria; cell; supervised learning; logistic regression; radial basis function network FCFM imaging; lung; bacteria; cell; supervised learning; logistic regression; radial basis function network

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Seth, S.; Akram, A.R.; Dhaliwal, K.; Williams, C.K.I. Estimating Bacterial and Cellular Load in FCFM Imaging. J. Imaging 2018, 4, 11.

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