Analysis and morphological comparison of the arteriolar and venular components of a microvascular network are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained with smooth muscle [Formula: see text]-actin. Classifiers trained on statistical and morphological features by supervised machine learning provided useful classification accuracy for differentiation of arterioles from venules, achieving an area under the receiver operating characteristic curve of 0.89. Feature selection was consistent across cross validation iterations, and a small set of two features was required to achieve the reported performance, suggesting the generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample and paves the way for high-throughput analysis of the arteriolar and venular networks in the mouse.
Keywords: arteriole venule classification; feature analysis; machine learning; vasculature quantification; whole slide analysis.