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Using Machine Learning for Automatic Estimation of M. Smegmatis Cell Count from Fluorescence Microscopy Images

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Precision Health and Medicine (W3PHAI 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 843))

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Abstract

Relapse in Tuberculosis (TB) patients represents an important challenge to improve treatment. A large number of patients undergo relapse even after what was thought to be a successful treatment. Lipid rich (LR) bacteria, surviving treatment, are thought to play a key role in patient relapse. The presence of bacteria with intracellular lipid bodies in patients sputum was linked to higher risk of poor treatment outcome. LR bacteria can be stained and detected using fluorescence microscopy. However, manual counting of bacteria makes this method too labour intensive and potentially biased to be routinely used in practice or to foster large-scale data sets which would inform and drive future research efforts. In this paper we propose a new algorithm for automatic estimation of the number of bacteria present in images generated with fluorescence microscopy. Our approach comprises elements of image processing, computer vision and machine learning. We demonstrated the effectiveness of the method by testing it on fluorescence microscopy images of in vitro grown M. smegmatis cells stained with Nile red.

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Author information

Authors and Affiliations

  1. Cardiff University, Cardiff, CF10 3AT, Wales, UK

    Daniel Vente

  2. University of St Andrews, St Andrews, KY16 9SX, Scotland, UK

    Ognjen Arandjelović, Vincent O. Baron, Evelin Dombay & Stephen H. Gillespie

Authors
  1. Daniel Vente
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  2. Ognjen Arandjelović
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  3. Vincent O. Baron
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  4. Evelin Dombay
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  5. Stephen H. Gillespie
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Corresponding author

Correspondence to Ognjen Arandjelović .

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Editors and Affiliations

  1. Department of Pediatrics, The University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Memphis, TN, USA

    Arash Shaban-Nejad

  2. School of Nursing, University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

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Vente, D., Arandjelović, O., Baron, V.O., Dombay, E., Gillespie, S.H. (2020). Using Machine Learning for Automatic Estimation of M. Smegmatis Cell Count from Fluorescence Microscopy Images. In: Shaban-Nejad, A., Michalowski, M. (eds) Precision Health and Medicine. W3PHAI 2019. Studies in Computational Intelligence, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-030-24409-5_6

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