Digital Image Analysis of Heterogeneous Tuberculosis Pulmonary Pathology in Non-Clinical Animal Models using Deep Convolutional Neural Networks

Sci Rep. 2020 Apr 8;10(1):6047. doi: 10.1038/s41598-020-62960-6.


Efforts to develop effective and safe drugs for treatment of tuberculosis require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology therefore has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations, while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models, this approach has also broader applications to other disease models and tissues. The full source code and documentation is available from

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Disease Models, Animal
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Lung / diagnostic imaging*
  • Lung / pathology
  • Mice
  • Mice, Inbred C3H
  • Mycobacterium tuberculosis / physiology*
  • Neural Networks, Computer
  • Software
  • Tuberculosis, Pulmonary / diagnostic imaging*
  • Tuberculosis, Pulmonary / pathology