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Proceedings Paper

Segmentation of mycobacterium tuberculosis bacilli clusters from acid-fast stained lung biopsies: a deep learning approach
Author(s): Thomas E. Tavolara; Muhammad Khalid Khan Niazi; Gillian Beamer; Metin N. Gurcan
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Paper Abstract

Individual factors that result in susceptibility and morbidity due to Mycobacterium tuberculosis (M.tb) infection are not clearly defined in humans or in animal models of disease. This leads to challenges for example, differentiating life-long control of latent Mycobacterium tuberculosis infection from active tuberculosis disease and accurately predicting who will develop active tuberculosis. As a part of a larger study to identify biomarkers to differentiate these states and predict long-term clinical outcomes, here we present a deep learning approach to segment Mycobacterium tuberculosis bacilli clusters from acid-fast stained lung sections from experimentally infected mice. In a 4-fold cross-validation of 178 slides, our method demonstrates ability to segment bacilli with median dice coefficient of 0.8. In tandem with improved segmentation and cluster analysis association with specific anatomical regions, our method will be able to differentiate between clinical states of M.tb infection.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200E (16 March 2020); doi: 10.1117/12.2549016
Show Author Affiliations
Thomas E. Tavolara, Wake Forest School of Medicine (United States)
Muhammad Khalid Khan Niazi, Wake Forest School of Medicine (United States)
Gillian Beamer, Tufts Univ. Cummings School of Veterinary Medicine (United States)
Metin N. Gurcan, Wake Forest School of Medicine (United States)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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