Department of Informatics, University of Oslo, Gaustadalléen 21, 0349 Oslo, Norway.
Department of Mathematics, University of Oslo, Blindern 0316 Oslo, Norway.
Artif Intell Med. 2022 May;127:102287. doi: 10.1016/j.artmed.2022.102287. Epub 2022 Mar 28.
Capillaries are the smallest vessels in the body which are responsible for delivering oxygen and nutrients to surrounding cells. Various life-threatening diseases are known to alter the density of healthy capillaries and the flow velocity of erythrocytes within the capillaries. In previous studies, capillary density and flow velocity were manually assessed by trained specialists. However, manual analysis of a standard 20-s microvascular video requires 20 min on average and necessitates extensive training. Thus, manual analysis has been reported to hinder the application of microvascular microscopy in a clinical environment. To address this problem, this paper presents a fully automated state-of-the-art system to quantify skin nutritive capillary density and red blood cell velocity captured by handheld-based microscopy videos. The proposed method combines the speed of traditional computer vision algorithms with the accuracy of convolutional neural networks to enable clinical capillary analysis. The results show that the proposed system fully automates capillary detection with an accuracy exceeding that of trained analysts and measures several novel microvascular parameters that had eluded quantification thus far, namely, capillary hematocrit and intracapillary flow velocity heterogeneity. The proposed end-to-end system, named CapillaryNet, can detect capillaries at ~0.9 s per frame with ~93% accuracy. The system is currently used as a clinical research product in a larger e-health application to analyse capillary data captured from patients suffering from COVID-19, pancreatitis, and acute heart diseases. CapillaryNet narrows the gap between the analysis of microcirculation images in a clinical environment and state-of-the-art systems.
毛细血管是人体中最小的血管，负责向周围细胞输送氧气和营养物质。许多危及生命的疾病已知会改变健康毛细血管的密度和毛细血管内红细胞的流动速度。在以前的研究中，毛细血管密度和血流速度是由经过训练的专家手动评估的。然而，对标准的 20 秒微血管视频进行手动分析平均需要 20 分钟，并且需要广泛的培训。因此，据报道，手动分析阻碍了微血管显微镜在临床环境中的应用。为了解决这个问题，本文提出了一种全自动的最先进的系统，用于量化手持式显微镜视频捕获的皮肤营养毛细血管密度和红细胞速度。该方法结合了传统计算机视觉算法的速度和卷积神经网络的准确性，以实现临床毛细血管分析。结果表明，该系统可以完全自动检测毛细血管，其准确性超过了经过训练的分析师，并且可以测量迄今为止难以量化的几个新的微血管参数，即毛细血管血细胞比容和毛细血管内血流速度异质性。该名为 CapillaryNet 的端到端系统可以以约 0.9 秒/帧的速度检测毛细血管，准确率约为 93%。该系统目前用作更大的电子健康应用程序中的临床研究产品，用于分析患有 COVID-19、胰腺炎和急性心脏病的患者捕获的毛细血管数据。CapillaryNet 缩小了临床环境中微循环图像分析与最先进系统之间的差距。
