Deep learning and computer vision techniques for microcirculation analysis: A review

Maged Helmy; Trung Tuyen Truong; Eric Jul; Paulo Ferreira

doi:10.1016/j.patter.2022.100641

Deep learning and computer vision techniques for microcirculation analysis: A review

Patterns (N Y). 2022 Dec 1;4(1):100641. doi: 10.1016/j.patter.2022.100641. eCollection 2023 Jan 13.

Authors

Maged Helmy¹, Trung Tuyen Truong², Eric Jul^{1

3}, Paulo Ferreira¹

Affiliations

¹ Department of Informatics, University of Oslo, Oslo, Norway.
² Department of Mathematics, University of Oslo, Oslo, Norway.
³ Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.

Abstract

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.

Keywords: image analysis; literature survey; microcirculation analysis.

Publication types

Review