Digital Image Analyses on Whole-Lung Slides in Mouse Models of Acute Pneumonia

Descriptive histopathology of mouse models of pneumonia is essential in assessing the outcome of infections, molecular manipulations, or therapies in the context of whole lungs. Quantitative comparisons between experimental groups, however, have been limited to laborious stereology or ill-defined scoring systems that depend on the subjectivity of a more or less experienced observer. Here, we introduce self-learning digital image analyses that allow us to transform optical information from whole mouse lung sections into statistically testable data. A pattern-recognition–based software and a nuclear count algorithm were adopted to quantify user-defined pathologies from whole slide scans of lungs infected with Streptococcus pneumoniae or influenza A virus compared with PBS-challenged lungs. The readout parameters “relative area affected” and “nuclear counts per area” are proposed as relevant criteria for the quantification of lesions from hematoxylin and eosin–stained sections, also allowing for the generati...