We report an automated method for characterization of microvessel morphology in micrographs of brain tissue sections to enable the facile, quantitative analysis of vascular differences across large datasets consisting of hundreds of images with thousands of blood vessel objects. Our objective is to show that virtual 3D parametric models of vasculature are adequately capable of representing the morphology of naturally acquired data in neuropathology. In this work, we focus on three distinct morphologies that are most frequently observed in formalin-fixed, paraffin-embedded (FFPE) human brain tissue samples: single blood vessels showing no (or collapsed) significant lumen ('RoundLumen-'); single blood vessels with distinct lumen ('RoundLumen+'); two blood vessels bundled together in close proximity ('Twins'). The analysis involves extraction of features using pre-trained convolutional neural networks. A hierarchical classification is performed to distinguish single blood vessels (RoundLumen) from Twins; followed by a more granular classification between RoundLumen- and RoundLumen+. A side-by-side comparison of the virtual and natural data models is presented. We observed that classification models built on the virtual data perform well achieving accuracies of 92.8% and 98.3% for the two aforementioned classification tasks respectively.