A semi-automated method for unbiased alveolar morphometry: validation in a bronchopulmonary dysplasia model

Thomas Salaets, Bieke Tack, Andre Gie, Benjamin Pavie, Nikhil Sindhwani, Julio Jimenez, Yannick Regin, Karel Allegaert, Jan Deprest, Jaan Toelen

Research output: Contribution to journalA1: Web of Science-articlepeer-review

50 Downloads (Pure)


Reproducible and unbiased methods to quantify alveolar structure are important for research on many lung diseases. However, manually estimating alveolar structure through stereology is time consuming and inter-observer variability is high. The objective of this work was to develop and validate a fast, reproducible and accurate (semi-)automatic alternative. A FIJI-macro was designed that automatically segments lung images to binary masks, and counts the number of test points falling on tissue and the number of intersections of the air-tissue interface with a set of test lines. Manual selection remains necessary for the recognition of non-parenchymal tissue and alveolar exudates. Volume density of alveolar septa ([Formula: see text]) and mean linear intercept of the airspaces (Lm) as measured by the macro were compared to theoretical values for 11 artificial test images and to manually counted values for 17 lungs slides using linear regression and Bland-Altman plots. Inter-observer agreement between 3 observers, measuring 8 lungs both manually and automatically, was assessed using intraclass correlation coefficients (ICC). [Formula: see text] and Lm measured by the macro closely approached theoretical values for artificial test images (R2 of 0.9750 and 0.9573 and bias of 0.34% and 8.7%). The macro data in lungs were slightly higher for [Formula: see text] and slightly lower for Lm in comparison to manually counted values (R2 of 0.8262 and 0.8288 and bias of -6.0% and 12.1%). Visually, semi-automatic segmentation was accurate. Most importantly, manually counted [Formula: see text] and Lm had only moderate to good inter-observer agreement (ICC 0.859 and 0.643), but agreements were excellent for semi-automatically counted values (ICC 0.956 and 0.900). This semi-automatic method provides accurate and highly reproducible alveolar morphometry results. Future efforts should focus on refining methods for automatic detection of non-parenchymal tissue or exudates, and for assessment of lung structure on 3D reconstructions of lungs scanned with microCT.

Original languageEnglish
Article numbere0239562
JournalPLoS ONE
Issue number9
Number of pages13
Publication statusPublished - 2020




Dive into the research topics of 'A semi-automated method for unbiased alveolar morphometry: validation in a bronchopulmonary dysplasia model'. Together they form a unique fingerprint.

Cite this