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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: Jul 27, 2025

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

Blood flow characterization in nailfold capillary using optical flow-assisted two-stream network and spatial-temporal

Shupin Chen¹, Dan Wei², Shenming Gu²

¹School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, People's Republic of China.

Biomedical Physics & Engineering Express

|June 5, 2023

Summary

This study introduces an AI-powered method for analyzing nailfold capillary blood flow. The new technique accurately segments blood vessels and measures flow velocity, improving microcirculation assessment.

Keywords:

U-Net multimodality nailfold capillaries optical flow two-stream

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Last Updated: Jul 27, 2025

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

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Published on: November 18, 2019

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Published on: August 30, 2017

Area of Science:

Medical Imaging
Biomedical Engineering
Machine Learning

Background:

Nailfold capillary blood flow velocity is a key microcirculation indicator.
Manual analysis is labor-intensive and prone to errors.
Machine learning offers a solution for automated image processing and diagnosis.

Purpose of the Study:

To develop an optical flow-assisted two-stream network for nailfold blood vessel segmentation.
To improve the accuracy and integrity of blood vessel segmentation.
To establish an effective workflow for measuring nailfold capillary blood flow velocity.

Main Methods:

Utilized a two-stream network architecture inspired by Convolutional Networks.
Employed U-Net as the spatial stream and dense optical flow as the temporal stream.
Constructed spatial-temporal (ST) images for blood flow velocity evaluation.

Main Results:

Achieved high segmentation accuracy (94.01%), Dice score (0.8099), and IoU score (0.6806).
Demonstrated that optical flow information enhances blood vessel segmentation integrity.
Evaluated blood flow velocity consistent with established values.

Conclusions:

The proposed two-stream network offers a novel approach for nailfold capillary blood vessel segmentation.
The integrated workflow provides an effective method for measuring microcirculation blood flow velocity.
This AI-driven approach enhances the diagnostic capabilities for microcirculation assessment.