Localization and phenotyping of tuberculosis bacteria using a combination of deep learning and SVMs

Marios Zachariou*, Ognjen Arandjelović, Evelin Dombay, Wilber Sabiiti, Bariki Mtafya, Nyanda Elias Ntinginya, Derek J. Sloan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Article number107573
Number of pages13
JournalComputers in Biology and Medicine
Early online date31 Oct 2023
Publication statusPublished - 1 Dec 2023


  • Microscopy
  • Machine learning
  • Fluorescence
  • Feature descriptors
  • MSVR
  • Regression
  • Deep learning
  • Treatment monitoring
  • Mycobacterium tuberculosis


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