Deep learning based automation of mean linear intercept quantification in COPD research

Frontiers in Big Data, vol. 8, pp. 1461016

Abstract

Chronic obstructive pulmonary disease (COPD), a major cause of global mortality, necessitates novel therapies targeting lung function and remodeling. Their effect on emphysema formation is initially investigated using mouse models by analyzing histological lung sections. The extent of airspace enlargement that is characteristic for emphysema is quantified by manual assessment of the mean linear intercept (MLI) across multiple histological microscopy images. Besides being tedious and cost intensive, this manual task lacks scientific comparability due to complexity and subjectivity. In order to continue with the well-established practice and to preserve the comparability of study results, we propose a deep learning-based approach for automating the determination of MLI in histological lung sections utilizing the AutoML software AIxCell which is specialized for the domain of semantic segmentation-based cell culture and tissue analysis. We develop and evaluate our image processing pipeline on stained histological microscope images that stem from a study including two groups of C57BL/6 mice where one group was exposed to cigarette smoke while the control group was not. The results indicate that the AIxCell segmentation algorithm achieves excellent performance, with IoU scores consistently exceeding 90%. Furthermore, the automated approach consistently yields higher MLI values compared to the manually generated values. However, the consistent nature of this discrepancy suggests that the automated approach can be reliably employed without any limitations. Moreover, it demonstrates statistical significance in distinguishing between smoker's and non-smoker's lungs.

Authors 7

1. Lars Leyendecker corresponding Aachen Department of Production Quality Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Department of Production Quality, Production Metrology and Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany

2. Anna Louisa Weltin Aachen Department of Production Quality Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Department of Production Quality, Production Metrology and Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany

3. Florian Nienhaus Aachen Department of Production Quality Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Department of Production Quality, Production Metrology and Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany

4. Michaela Matthey

   Ruhr University Bochum

   Affiliation as printed

   Department of Systems Physiology, Medical Faculty, Ruhr University of Bochum, Bochum, Germany

5. Bastian Nießing Aachen Department of Production Quality Fraunhofer Institute for Production Technology IPT

   Fraunhofer Institute for Production Technology IPT

   Affiliation as printed

   Department of Production Quality, Production Metrology and Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany

6. Daniela Wenzel

   University of Bonn · Ruhr University Bochum

   Affiliation as printed

   Department of Systems Physiology, Medical Faculty, Ruhr University of Bochum, Bochum, Germany

   Medical Faculty, Institute of Physiology I, University of Bonn, Bonn, Germany

7. Robert Heinrich Schmitt Aachen Department of Production Quality Fraunhofer Institute for Production Technology IPT Intelligence in Quality Sensing Laboratory for Machine Tools and Production Engineering WZL

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Department of Production Quality, Production Metrology and Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany

   Intelligence in Quality Sensing, Laboratory for Machine Tools and Production Engineering WZL of RWTH Aachen, Aachen, Germany