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Open AccessArticle

Automatic Detection of Granuloma Necrosis in Pulmonary Tuberculosis Using a Two-Phase Algorithm: 2D-TB

1
Department of Research, Development and Technology, Republic of Turkey Ministry of National Defence, 06100 Ankara, Turkey
2
Department of Internal Medicine, School of Medicine, Wake Forest University, Winston-Salem, NC 27109, USA
3
Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 01536, USA
*
Author to whom correspondence should be addressed.
Microorganisms 2019, 7(12), 661; https://doi.org/10.3390/microorganisms7120661
Received: 9 October 2019 / Revised: 5 November 2019 / Accepted: 8 November 2019 / Published: 7 December 2019
Granuloma necrosis occurs in hosts susceptible to pathogenic mycobacteria and is a diagnostic visual feature of pulmonary tuberculosis (TB) in humans and in super-susceptible Diversity Outbred (DO) mice infected with Mycobacterium tuberculosis. Currently, no published automated algorithms can detect granuloma necrosis in pulmonary TB. However, such a method could reduce variability, and transform visual patterns into quantitative data for statistical and machine learning analyses. Here, we used histopathological images from super-susceptible DO mice to train, validate, and performance test an algorithm to detect regions of cell-poor necrosis. The algorithm, named 2D-TB, works on 2-dimensional histopathological images in 2 phases. In phase 1, granulomas are detected following background elimination. In phase 2, 2D-TB searches within granulomas for regions of cell-poor necrosis. We used 8 lung sections from 8 different super-susceptible DO mice for training and 10-fold cross validation. We used 13 new lung sections from 10 different super-susceptible DO mice for performance testing. 2D-TB reached 100.0% sensitivity and 91.8% positive prediction value. Compared to an expert pathologist, agreement was 95.5% and there was a statistically significant positive correlation for area detected by 2D-TB and the pathologist. These results show the development, validation, and accurate performance of 2D-TB to detect granuloma necrosis. View Full-Text
Keywords: tuberculosis; granuloma; necrosis; algorithm; machine learning tuberculosis; granuloma; necrosis; algorithm; machine learning
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Kus, P.; Gurcan, M.N.; Beamer, G. Automatic Detection of Granuloma Necrosis in Pulmonary Tuberculosis Using a Two-Phase Algorithm: 2D-TB. Microorganisms 2019, 7, 661.

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