Artificial vascular image generation using blood vessel texture maps.

Background: Current methods for identifying blood vessels in digital images typically involve training neural networks on pixel-wise annotated data. However, manually outlining whole vessel trees in images tends to be very costly. One approach for reducing the amount of manual annotation is to pre-train networks on artificially generated vessel images.

Unbiased analysis of mouse brain endothelial networks from two- or three-dimensional fluorescence images.

A growing body of research supports the significant role of cerebrovascular abnormalities in neurological disorders. As these insights develop, standardized tools for unbiased and high-throughput quantification of cerebrovascular structure are needed. We provide a detailed protocol for performing immunofluorescent labeling of mouse brain vessels, using thin ( ) or thick (50 to ) tissue sections, followed respectively by two- or three-dimensional (2D or 3D) unbiased quantification of vessel density, branching, and tortuosity using digital image processing algorithms.