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.