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Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

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

Lars Leyendecker¹, Anna Louisa Weltin¹, Florian Nienhaus¹

¹Department of Production Quality, Production Metrology and Bio-Adaptive Production, Fraunhofer Institute for Production Technology IPT, Aachen, Germany.

Frontiers in Big Data

|June 25, 2025

Summary

This study introduces an automated deep learning method using AIxCell to measure emphysema in mouse lungs. The AI-driven approach accurately quantifies lung changes, offering a reliable alternative to manual methods for COPD research.

Keywords:

automated machine learning deep learning mean linear intercept (MLI)microscopy pulmonary disease semantic segmentation

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Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

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Published on: June 20, 2025

Area of Science:

Pulmonary Medicine
Computational Pathology
Biomedical Imaging

Background:

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide, requiring new treatments.
Emphysema, a key component of COPD, involves lung airspace enlargement.
Manual quantification of emphysema via mean linear intercept (MLI) is subjective and labor-intensive.

Purpose of the Study:

To develop and validate a deep learning-based automated approach for determining MLI in histological lung sections.
To assess the performance of the AIxCell software for automated MLI measurement.
To compare automated MLI values with manual assessments and evaluate its utility in distinguishing between smoke-exposed and control mouse lungs.

Main Methods:

A deep learning pipeline using the AIxCell AutoML software was developed for semantic segmentation of histological lung sections.
The pipeline was trained and evaluated on stained histological images from C57BL/6 mice exposed to cigarette smoke and a control group.
Image processing involved quantifying the mean linear intercept (MLI) to assess airspace enlargement characteristic of emphysema.

Main Results:

The AIxCell segmentation algorithm achieved high performance with Intersection over Union (IoU) scores consistently above 90%.
The automated method yielded consistently higher MLI values than manual measurements, indicating reliable quantification.
The automated MLI determination demonstrated statistical significance in differentiating between smoke-exposed and control mouse lungs.

Conclusions:

The AIxCell-based automated approach provides a reliable, objective, and efficient method for quantifying MLI in emphysema research.
This automated method preserves comparability with existing manual assessments while overcoming their limitations.
The tool is effective for distinguishing lung pathology in COPD models, supporting the development of novel therapies.