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 () 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.

可重复且无偏见的量化肺泡结构的方法对许多肺部疾病的研究很重要。然而，通过立体学手动估计肺泡结构是费时的，并且观察者之间的可变性很高。这项工作的目的是开发和验证一种快速，可重现和准确的（半）自动替代方案。FIJI宏设计用于将肺部图像自动分割为二进制蒙版，并计数落在组织上的测试点的数量以及空气-组织界面与一组测试线的相交的数量。手动选择对于识别非实质组织和肺泡渗出物仍然是必需的。肺泡间隔的容积密度（）和空域的平均线性截距（Lm宏测量的）与11个人工测试图像的理论值进行了比较，并使用线性回归和Bland-Altman图与17个肺部载玻片的手动计数值进行了比较。使用类内相关系数（ICC）评估了3位观察者之间的观察员之间的共识，他们手动和自动测量8个肺。宏测量的Lm和Lm接近人工测试图像的理论值（R ²为0.9750和0.9573，偏差为0.34％和8.7％）。与手动计数的值相比，Lm的肺部宏观数据略高，而Lm略低（R ²0.8262和0.8288，偏差为-6.0％和12.1％）。在视觉上，半自动分割是准确的。最重要的是，手动计数和Lm仅具有中等到良好的观察者之间的一致性（ICC为0.859和0.643），但对于半自动计数值（ICC为0.956和0.900），一致性很好。这种半自动方法可提供准确且高度可重复的牙槽形态测量结果。未来的工作应集中在自动检测非实质组织或渗出物的精炼方法上，以及在用microCT扫描的3D肺重建物上评估肺结构的方法。