Data and code from: Wide-Angle Lung Experiment Segmentation (WALES): A novel methodology for quantitative assessment of lung pathology in model systems

For pre-clinical studies, the standard practice for evaluating lung injury usually involves an assessment of pulmonary histopathology by a certified pathologist. This is typically accomplished by light microscopy using a semi-quantitative 4-point scale. In contrast, automated image analysis software allows a more quantitative assessment, though inherent limitations with such automated programs can produce misleading conclusions. For example, specific imaging features may be incorrectly scored or classified within the specimen because of the complex architecture and heterogenous structures present in the lung. Additionally, tissue processing and handling may further introduce artifacts and inconsistencies that affect automated analysis. To address these limitations, we developed a novel lung image analysis program, Wide Angle Lung Experiment Segmentation (WALES), which employs Meta’s Segment Anything Model to provide semi-automated masking and relative density analysis to efficiently quantify lung injury. Density analysis using WALES effectively delineated varying severities of lung injury, not achieved using more standard methods. WALES is widely applicable for many preclinical lung injury models.