  Capillaries are the smallest vessels in the body 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-second microvascular video requires 20 minutes 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
$\sim$0.9 seconds per frame with $\sim$93\% accuracy. The system is currently
being 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.
