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Blood Flow

Blood Flow

Blood is pumped by the heart into the aorta, the largest artery in the body, and then into increasingly smaller arteries, arterioles, and capillaries. The velocity of blood flow decreases with increased cross-sectional blood vessel area. As blood returns to the heart through venules and veins, its velocity increases. The movement of blood is encouraged by smooth muscle in the vessel walls, the movement of skeletal muscle surrounding the vessels, and one-way valves that prevent backflow.

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Related Experiment Video

Updated: Nov 10, 2025

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Published on: April 25, 2013

Manual and Automatic Image Analysis Segmentation Methods for Blood Flow Studies in Microchannels.

Violeta Carvalho¹, Inês M Gonçalves², Andrews Souza³

¹Mechanical Engineering and Resource Sustainability Center (MEtRICs), Mechanical Engineering Department, University of Minho, 4800-058 Guimarães, Portugal.

|April 3, 2021

Summary

Automated image analysis methods were developed to track red blood cells (RBCs) and measure the cell-free layer in microfluidic blood flow studies. These methods offer accurate and efficient alternatives to time-consuming manual analysis.

Keywords:

automatic methods biomicrofluidics blood flow image analysis manual methods particle tracking red blood cells

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Last Updated: Nov 10, 2025

Micro-particle Image Velocimetry for Velocity Profile Measurements of Micro Blood Flows

Published on: April 25, 2013

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Published on: June 4, 2015

Area of Science:

Biomedical Engineering
Microfluidics
Image Analysis

Background:

High-speed video microscopy generates crucial data for blood flow studies.
Manual analysis of microfluidic blood flow data is reliable but time-consuming and subjective.
Accurate tracking of red blood cells (RBCs) and measurement of the cell-free layer are vital.

Purpose of the Study:

To present and discuss automated image analysis methods for RBC tracking.
To introduce techniques for measuring cell-free layer thickness in microchannels.
To demonstrate the feasibility of automated methods for accurate data acquisition in microcirculation studies.

Main Methods:

Development of two distinct automated image analysis methods.
Application of methods to track individual RBCs in glass capillaries.
Measurement of cell-free layer thickness in various microchannel types.
Comparison of automated methods against traditional manual analysis techniques.

Main Results:

Automated methods provide accurate data acquisition for RBC tracking.
Automated techniques effectively measure cell-free layer thickness.
The developed automated methods are feasible for microfluidic blood flow studies.
Automated analysis offers a significant improvement in efficiency over manual methods.

Conclusions:

Automated image analysis is crucial for efficient and objective blood flow studies.
The presented methods enable accurate tracking of RBCs and cell-free layer measurement.
Automated approaches overcome limitations of manual analysis in microfluidic research.