Abstract
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 https://Github.com/TB-imaging/LIRA.
Highlights
Murine models are employed in TB research due to their small size and low cost, and in addition, their physiology and genetics are well understood
For mouse models used in preclinical testing for TB, the treatment effect on pulmonary pathology is generally examined on microscopic images using a histological grading system by a veterinary pathologist specialized in TB38
This digital image analysis software package was developed as a modular neural network[55,56,57,58], consisting of three convolutional neural networks (CNN), each optimized for a specific sub-task, together with two human intervention checkpoints to limit the probability of misclassification (Figs. 1, 2)
Summary
Murine models are employed in TB research due to their small size and low cost, and in addition, their physiology and genetics are well understood. We propose a TB lesion machine learning classifier that follows established classification criteria developed in our laboratory which distinguishes three different lesion types that develop in the C3HeB/FeJ mouse TB infection model[20,21]. This digital image analysis software package was developed as a modular neural network[55,56,57,58], consisting of three CNNs, each optimized for a specific sub-task, together with two human intervention checkpoints to limit the probability of misclassification LIRA was designed to work in conjunction with pathologists with the goal to improve the analysis of pulmonary pathology to be more efficient, accurate, and reproducible regardless of the individual analyzing the whole slide images
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