ArticleFrontiers in big data2025
Deep learning based automation of mean linear intercept quantification in COPD research.
Article in Frontiers in big data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Uncovering underdiagnosed emphysema: comparative assessment using clinical CT and micro-CT.European radiology · 2026Article
- AlveolEye: rapid and precise lung morphometry guided by computer vision.American journal of physiology. Lung cellular and molecular physiology · 2026Article
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Authors and funding
7 authors.
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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
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