Estimating phenotypic characteristics of tuberculosis bacteria
MetadataShow full item record
Successful treatment of pulmonary tuberculosis (TB) depends on early diagnosis and careful monitoring of treatment response. Identification of acid-fast bacilli by fluorescence microscopy of sputum smears is a common tool for both tasks. Microscopy-based analysis of the intracellular lipid content and dimensions of individual Mycobacterium tuberculosis (Mtb) cells also describe phenotypic changes which may improve our biological understanding of antibiotic therapy for TB. However, fluorescence microscopy is a challenging, time-consuming and subjective procedure. In this work, we automate examination of fields of view (FOVs) from microscopy images to determine the lipid content and dimensions (length and width) of Mtb cells. We introduce an adapted variation of the UNet model to efficiently localizing bacteria within FOVs stained by two fluorescence dyes; auramine O to identify Mtb and LipidTox Red to identify intracellular lipids. Thereafter, we propose a feature extractor in conjunction with feature descriptors to extract a representation into a support vector multi-regressor and estimate the length and width of each bacterium. Using a real-world data corpus from Tanzania, the proposed method i) outperformed previous methods for bacterial detection with a 4% improvement in the Jaccard index and ii) estimated the cell length and width with a root mean square error of less than 0.01%. Our network can be used to examine phenotypic characteristics of Mtb cells visualised by fluorescence microscopy, improving consistency and time efficiency of this procedure compared to manual methods.
Zachariou , M , Arandjelović , O , Dombay , E , Sabiiti , W , Mtafya , B , Ntinginya , N E & Sloan , D J 2023 , ' Estimating phenotypic characteristics of tuberculosis bacteria ' , Computers in Biology and Medicine , vol. 167 , 107573 . https://doi.org/10.1016/j.compbiomed.2023.107573
Computers in Biology and Medicine
Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
DescriptionFunding: Supported by a Wellcome Trust Institutional Strategic Support Fund award to the University of St Andrews, grant code 204821/Z/16/Z.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.