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Open AccessArticle

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

Department of Research, Development and Technology, Republic of Turkey Ministry of National Defence, 06100 Ankara, Turkey
Department of Internal Medicine, School of Medicine, Wake Forest University, Winston-Salem, NC 27109, USA
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;
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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