Alterations in vascular networks, including angiogenesis and capillary regression, play key roles in disease, wound healing, and development. The spatial structures of blood vessels can be captured through imaging, but effective characterization of network architecture requires both metrics for quantification and software to carry out the analysis in a high‐throughput and unbiased fashion. We present Rapid Editable Analysis of Vessel Elements Routine (REAVER), an open‐source tool that researchers can use to analyze high‐resolution 2D fluorescent images of blood vessel networks, and assess its performance compared to alternative image analysis programs. Using a dataset of manually analyzed images from a variety of murine tissues as a ground‐truth, REAVER exhibited high accuracy and precision for all vessel architecture metrics quantified, including vessel length density, vessel area fraction, mean vessel diameter, and branchpoint count, along with the highest pixel‐by‐pixel accuracy for the segmentation of the blood vessel network. In instances where REAVER's automated segmentation is inaccurate, we show that combining manual curation with automated analysis improves the accuracy of vessel architecture metrics. REAVER can be used to quantify differences in blood vessel architectures, making it useful in experiments designed to evaluate the effects of different external perturbations (eg, drugs or disease states).

中文翻译：

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REAVER：改进高分辨率血管网络图像分析的程序。

血管网络的改变，包括血管生成和毛细血管退化，在疾病、伤口愈合和发育中起着关键作用。血管的空间结构可以通过成像捕获，但网络架构的有效表征需要量化指标和软件以高通量和无偏见的方式进行分析。我们介绍了血管元素程序的快速可编辑分析 (REAVER)，这是一种开源工具，研究人员可以使用它来分析血管网络的高分辨率 2D 荧光图像，并与其他图像分析程序相比评估其性能。使用来自各种鼠类组织的手动分析图像数据集作为基本事实，REAVER 对量化的所有血管结构指标表现出高精度和精确度，包括血管长度密度、血管面积分数、平均血管直径和分支点计数，以及血管网络分割的最高逐像素精度。在 REAVER 的自动分割不准确的情况下，我们表明将手动管理与自动分析相结合可以提高血管结构指标的准确性。REAVER 可用于量化血管结构的差异，使其在旨在评估不同外部扰动（例如，药物或疾病状态）影响的实验中非常有用。我们表明，将手动管理与自动分析相结合可以提高血管结构指标的准确性。REAVER 可用于量化血管结构的差异，使其在旨在评估不同外部扰动（例如，药物或疾病状态）影响的实验中非常有用。我们表明，将手动管理与自动分析相结合可以提高血管结构指标的准确性。REAVER 可用于量化血管结构的差异，使其在旨在评估不同外部扰动（例如，药物或疾病状态）影响的实验中非常有用。