Capillaries are the smallest vessels in the body responsible for the delivery of oxygen and nutrients to the surrounding cells. Various diseases have been shown to alter the density of nutritive capillaries and the flow velocity of erythrocytes. In previous studies, capillary density and flow velocity have been assessed manually by trained specialists. Manual analysis of a 20-second long microvascular video takes on average 20 minutes and requires extensive training. Several studies have reported that manual analysis hinders the application of microvascular microscopy in a clinical setting. In this paper, we present a fully automated system, called CapillaryNet, that can automate microvascular microscopy analysis and thus enable the method to be used not just as a research tool, but also for clinical applications. Our method has been developed by acquiring microcirculation videos from 50 different subjects annotated by trained biomedical researchers. CapillaryNet detects capillaries with an accuracy comparable to trained researchers in less than 0.1% of the time taken by humans and measures several microvascular parameters that researchers were previously unable to quantify, i.e. capillary hematocrit and intra-capillary flow velocity heterogeneity.

翻译：向周围细胞提供氧气和养分的体体中最小的血管是负责向周围细胞提供氧气和养分的容器。各种疾病已经证明可以改变营养性毛毛毛毛的密度和红细胞的流速。在以前的研究中,由受过训练的专家对毛细密度和流速进行了手工评估。对20秒长的显微血管视频的人工分析平均需要20分钟,需要广泛的培训。一些研究报告说,人工分析妨碍在临床环境中应用显微血管显微镜。本文中,我们提出了一个完全自动化的系统,称为毛毛虫网,可以自动进行显微血管显微镜分析,从而使得这种方法不仅作为一种研究工具,而且能够用于临床应用。我们的方法是通过从50个不同学科获得显微透镜,并由受过训练的生物医学研究人员附加说明。毛细网探测毛细的毛血管,其精确度可与受过训练的研究人员相比,在人类所花时间的0.1%以下的时间里,并测量了研究人员以前无法量化的几项微血管参数,即毛毛透视和内部气轴流。