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

Maged Helmy*, Trung Tuyen Truong, Eric Jul, Paulo Ferreira

*Corresponding author for this work

Research output: Contribution to journalReviewResearchpeer-review

5 Citations (Scopus)
36 Downloads (Pure)

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.

Original languageEnglish
Article number100641
JournalPatterns
Volume4
Issue number1
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

Keywords

  • DSML1: Concept: Basic principles of a new data science output observed and reported
  • image analysis
  • literature survey
  • microcirculation analysis

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