REAVER: Improved Analysis of High-resolution Vascular Network Images Revealed Through Round-robin Rankings of Accuracy and Precision
Alterations in vascular networks, including angiogenesis and capillary regression, play key roles in disease, wound healing, and development. Imaging of microvascular networks can reveal their spatial structures, but effective study of network architecture requires methods to accurately quantify them using a variety of metrics. We present REAVER (Rapid Editable Analysis of Vessel Elements Routine), a freely available open source tool that researchers can use to analyze and quantify high resolution fluorescent images of blood vessel networks, and assess its performance compared to alternative state-of-the-art image analysis software programs. Top performing programs for each metric are identified by assigning a rank based on statistical multiple comparisons of accuracy and precision, modeled as matches in a round-robin style tournament. This comparison method yields a clearly defined and consistent standard for characterizing program performance, avoiding the use of non-standard ad hoc interpretations of multiple comparisons between programs. Using this comparison method and a dataset of manually analyzed images as a ground-truth, we show that REAVER was the top ranked program for both accuracy and precision for all metrics quantified, including vessel length density, vessel area fraction, mean vessel diameter, and branchpoint count. REAVER can be used to quantify differences in blood vessel architectures between study groups, which makes it particularly useful in experiments designed to evaluate the effects of different external perturbations (e.g. drugs or disease states).