Helmy Maged, Truong Trung Tuyen, Jul Eric, Ferreira Paulo
Department of Informatics, University of Oslo, Oslo, Norway.
Department of Mathematics, University of Oslo, Oslo, Norway.
Patterns (N Y). 2022 Dec 1;4(1):100641. doi: 10.1016/j.patter.2022.100641. eCollection 2023 Jan 13.
The analysis of microcirculation images has the potential to reveal early signs of life-threatening diseases such as sepsis. Quantifying the capillary density and the capillary distribution in microcirculation images can be used as a biological marker to assist critically ill patients. The quantification of these biological markers is labor intensive, time consuming, and subject to interobserver variability. Several computer vision techniques with varying performance can be used to automate the analysis of these microcirculation images in light of the stated challenges. In this paper, we present a survey of over 50 research papers and present the most relevant and promising computer vision algorithms to automate the analysis of microcirculation images. Furthermore, we present a survey of the methods currently used by other researchers to automate the analysis of microcirculation images. This survey is of high clinical relevance because it acts as a guidebook of techniques for other researchers to develop their microcirculation analysis systems and algorithms.
微循环图像分析有潜力揭示诸如败血症等危及生命疾病的早期迹象。量化微循环图像中的毛细血管密度和毛细血管分布可作为一种生物标志物，以辅助重症患者。这些生物标志物的量化工作强度大、耗时且存在观察者间差异。鉴于上述挑战，可使用几种性能各异的计算机视觉技术来自动分析这些微循环图像。在本文中，我们对50多篇研究论文进行了综述，并介绍了用于自动分析微循环图像的最相关且最有前景的计算机视觉算法。此外，我们还综述了其他研究人员目前用于自动分析微循环图像的方法。这项综述具有很高的临床相关性，因为它为其他研究人员开发其微循环分析系统和算法提供了技术指南。
