Segmentation of mycobacterium tuberculosis bacilli clusters from acid-fast stained lung biopsies: a deep learning approach

Thomas E. Tavolara; Muhammad Khalid Khan Niazi; Gillian Beamer; Metin N. Gurcan

doi:10.1117/12.2549016

16 March 2020 Segmentation of mycobacterium tuberculosis bacilli clusters from acid-fast stained lung biopsies: a deep learning approach

Thomas E. Tavolara, Muhammad Khalid Khan Niazi, Gillian Beamer, Metin N. Gurcan

Author Affiliations +

Proceedings Volume 11320, Medical Imaging 2020: Digital Pathology; 113200E (2020) https://doi.org/10.1117/12.2549016
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Thomas E. Tavolara, Muhammad Khalid Khan Niazi, Gillian Beamer, and Metin N. Gurcan "Segmentation of mycobacterium tuberculosis bacilli clusters from acid-fast stained lung biopsies: a deep learning approach", Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200E (16 March 2020); https://doi.org/10.1117/12.2549016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available