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Deep learning and computer vision techniques for microcirculation analysis: A review
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 quantific...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868679/ https://www.ncbi.nlm.nih.gov/pubmed/36699745 http://dx.doi.org/10.1016/j.patter.2022.100641 |
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author | Helmy, Maged Truong, Trung Tuyen Jul, Eric Ferreira, Paulo |
author_facet | Helmy, Maged Truong, Trung Tuyen Jul, Eric Ferreira, Paulo |
author_sort | Helmy, Maged |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9868679 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-98686792023-01-24 Deep learning and computer vision techniques for microcirculation analysis: A review Helmy, Maged Truong, Trung Tuyen Jul, Eric Ferreira, Paulo Patterns (N Y) Review 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. Elsevier 2022-12-01 /pmc/articles/PMC9868679/ /pubmed/36699745 http://dx.doi.org/10.1016/j.patter.2022.100641 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Review Helmy, Maged Truong, Trung Tuyen Jul, Eric Ferreira, Paulo Deep learning and computer vision techniques for microcirculation analysis: A review |
title | Deep learning and computer vision techniques for microcirculation analysis: A review |
title_full | Deep learning and computer vision techniques for microcirculation analysis: A review |
title_fullStr | Deep learning and computer vision techniques for microcirculation analysis: A review |
title_full_unstemmed | Deep learning and computer vision techniques for microcirculation analysis: A review |
title_short | Deep learning and computer vision techniques for microcirculation analysis: A review |
title_sort | deep learning and computer vision techniques for microcirculation analysis: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868679/ https://www.ncbi.nlm.nih.gov/pubmed/36699745 http://dx.doi.org/10.1016/j.patter.2022.100641 |
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