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

Research output: Contribution to journal › Review › Research › peer-review

Standard

Deep learning and computer vision techniques for microcirculation analysis : A review. / Helmy, Maged; Truong, Trung Tuyen; Jul, Eric; Ferreira, Paulo.

In: Patterns, Vol. 4, No. 1, 100641, 2023.

Research output: Contribution to journal › Review › Research › peer-review

Harvard

Helmy, M, Truong, TT, Jul, E & Ferreira, P 2023, 'Deep learning and computer vision techniques for microcirculation analysis: A review', Patterns, vol. 4, no. 1, 100641. https://doi.org/10.1016/j.patter.2022.100641

APA

Helmy, M., Truong, T. T., Jul, E., & Ferreira, P. (2023). Deep learning and computer vision techniques for microcirculation analysis: A review. Patterns, 4(1), [100641]. https://doi.org/10.1016/j.patter.2022.100641

Vancouver

Helmy M, Truong TT, Jul E, Ferreira P. Deep learning and computer vision techniques for microcirculation analysis: A review. Patterns. 2023;4(1). 100641. https://doi.org/10.1016/j.patter.2022.100641

Author

Helmy, Maged ; Truong, Trung Tuyen ; Jul, Eric ; Ferreira, Paulo. / Deep learning and computer vision techniques for microcirculation analysis : A review. In: Patterns. 2023 ; Vol. 4, No. 1.

Bibtex

@article{4fbeef52e17b48e09541d1b27840f8bb,
title = "Deep learning and computer vision techniques for microcirculation analysis: A review",
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 = "DSML1: Concept: Basic principles of a new data science output observed and reported, image analysis, literature survey, microcirculation analysis",
author = "Maged Helmy and Truong, {Trung Tuyen} and Eric Jul and Paulo Ferreira",
note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2023",
doi = "10.1016/j.patter.2022.100641",
language = "English",
volume = "4",
journal = "Patterns",
issn = "2666-3899",
publisher = "Cell Press",
number = "1",

}

RIS

TY - JOUR

T1 - Deep learning and computer vision techniques for microcirculation analysis

T2 - A review

AU - Helmy, Maged

AU - Truong, Trung Tuyen

AU - Jul, Eric

AU - Ferreira, Paulo

N1 - Publisher Copyright: © 2022 The Author(s)

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - DSML1: Concept: Basic principles of a new data science output observed and reported

KW - image analysis

KW - literature survey

KW - microcirculation analysis

UR - http://www.scopus.com/inward/record.url?scp=85146349055&partnerID=8YFLogxK

U2 - 10.1016/j.patter.2022.100641

DO - 10.1016/j.patter.2022.100641

M3 - Review

C2 - 36699745

AN - SCOPUS:85146349055

VL - 4

JO - Patterns

JF - Patterns

SN - 2666-3899

IS - 1

M1 - 100641

ER -

ID: 334654438