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

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dc.contributor.authorSalaets, Thomas
dc.contributor.authorTack, Bieke
dc.contributor.authorGie, André
dc.contributor.authorPavie, Benjamin
dc.contributor.authorSindhwani, Nikhil
dc.contributor.authorJiménez, Julio
dc.contributor.authorRegin, Yannick
dc.contributor.authorAllegaert, Karel
dc.contributor.authorDeprest, Jan
dc.contributor.authorToelen, Jean
dc.date.accessioned2021-08-09T21:04:38Z
dc.date.available2021-08-09T21:04:38Z
dc.date.issued2020-09
dc.description.abstractReproducible 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 () 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). 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 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 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.es
dc.identifier.citationPLoS ONE 15(9): e0239562es
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0239562 Ses
dc.identifier.urihttp://hdl.handle.net/11447/4268
dc.language.isoenes
dc.subjectPulmonary imaginges
dc.subjectMorphometryes
dc.subjectLung diseaseses
dc.titleA semi-automated method for unbiased alveolar morphometry: Validation in a bronchopulmonary dysplasia modeles
dc.typeArticlees

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