"Immunohistochemistry and immunofluorescence for vascular network analysis play a fundamental role in basic science, translational research, and clinical practice. Due to their versatility and specificity, these techniques are also widely used in the tissue engineering context, where the study of neovasculature is crucial to assess the function of artificial tissue surrogates. However, identifying vascularization in histological tissue images is time-consuming and markedly depends on the operator's experience. This study introduces ""Blood Vessel Detection – BVD"", an automatic and ready-to-use morphometrical tool for quantitative analysis of vasculature in fluorescent histological images. BVD is based on the extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. The performance of the BVD algorithm was tested on four different datasets: a set of phantom images and on three sets of histological sections from three separate in vivo studies that specifically focused on the characterization of angiogenesis. The first study analyzed was an example of a rat abdominal wall defect treated by a polymeric patch loaded with microparticles able to release an angiogenic factor1. The second case was a rat infarction model treated with a bilayer biohybrid patch composed of polymer and extracellular matrix2. Finally, the algorithm was utilized to quantify angiogenesis in the case of ectopic organ regeneration3. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, quantifying the number, the diameters, the perimeters, the areas, and the spatial distribution of blood vessels within all the considered datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis.
1 D'Amore, A. et al. Tissue Engineering Part A 24, 889-904 (2018).
2 Silveira-Filho, L. et al. JACC: Basic to Translational Science 6.5, 447-463 (2021)
3 Francipane, M. G. et al. The American journal of pathology 190, 252-269 (2020)"