Open-source analysis and visualization of segmented vasculature datasets with VesselVio

Vascular networks are fundamental components of biological systems. Quantitative analysis and observation of the features of these networks can improve our understanding of their roles in health and disease. Recent advancements in imaging technologies have enabled the generation of large-scale vasculature datasets, but barriers to analyzing these datasets remain. Modern analysis options are mainly limited to paid applications or open-source terminal-based software that requires programming knowledge with high learning curves. Here, we describe VesselVio, an open-source application developed to analyze and visualize pre-binarized vasculature datasets and pre-constructed vascular graphs. Vasculature datasets and graphs can be loaded with annotations and processed with custom parameters. Here, the program is tested on ground-truth datasets and is compared with current pipelines. The utility of VesselVio is demonstrated by the analysis of multiple formats of 2D and 3D datasets acquired with several imaging modalities, including annotated mouse whole-brain vasculature volumes. [Display omitted] •VesselVio is an application created to analyze segmented vasculature datasets•Custom analysis pipelines provide detailed network and segment features•Interactive dataset visualization enables result inspection and image creation•VesselVio can be freely downloaded or run from the terminal with a single line of code Modern imaging techniques and open-source image segmentation packages have led to the generation of large-scale and high-resolution vasculature datasets. However, open-source analysis packages for these datasets require programming knowledge, leading to an access barrier for some researchers. To overcome this barrier, we present VesselVio, a standalone and open-source application developed for the analysis and visualization of vasculature datasets. Bumgarner et al. present an open-source application, named VesselVio, that was designed to facilitate the process of vasculature dataset analysis. Their application provides an accessible method for researchers to extract and visualize detailed features from numerous formats of vasculature datasets, including annotated mouse-brain volumes.