VESNA: An Open-Source Tool for Automated 3D Vessel Segmentation and Network Analysis

Background Vasculature is an essential part of all tissues and organs and is involved in a wide range of different diseases. However, available software for blood vessel image analysis is often limited: Some only process two-dimensional data, others lack batch processing, putting a time burden on the user, while still others require tightly defined culturing methods and experimental conditions. This highlights the need for a software that has the ability to batch process three-dimensional image data and requires few and simple experimental preparation steps. Results We present VESNA, a Fiji (ImageJ) macro for automated segmentation and skeletonization of three-dimensional fluorescence images, enabling quantitative vascular network analysis. It requires only basic experimental preparation, making it highly adaptable to a wide range of possible applications across experimental goals and different tissue culturing methods. The macro’s potential is demonstrated on a range of different image data sets, from organoids with varying sizes, network complexities, and growth conditions to expanding to other 3D tissue culturing methods with an example of hydrogel-based cultures. Conclusions With its ability to process large amounts of 3D image data and its flexibility across experimental conditions, VESNA fulfills previously unmet needs in image processing of vascular structures and can be a valuable tool for a variety of experimental setups around three-dimensional vasculature, such as drug screening, research in tissue development and disease mechanisms.