mlsanityData to 14 Oct 2026

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

arXiv 2205.05493 ↗First posted 11 May 2022cs.CV cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 45 of its 172 references that are in the map. First posted on arXiv 11 May 2022.

Builds on

45 of 172 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

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