Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Review

TL;DR

This paper reviews over 50 research studies on computer vision techniques for automating microcirculation image analysis, aiming to improve early disease detection and reduce labor-intensive manual assessments.

Contribution

It provides a comprehensive survey of relevant algorithms and methods, serving as a guide for future development in microcirculation image analysis.

Findings

Identifies promising computer vision algorithms for microcirculation analysis

Highlights current challenges and gaps in automation methods

Provides a curated overview of existing research approaches

Abstract

The analysis of microcirculation images has the potential to reveal early signs of life-threatening diseases like 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.