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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: Jun 30, 2026

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

Full-scale representation guided network for retinal vessel segmentation.

Sunyong Seo¹, Sangwook Yoo¹, Huisu Yoon^2,3

¹lululab Inc., AI R&D Center, Seoul, 06054, Republic of Korea.

BMC Medical Imaging

|November 22, 2025

Summary

This summary is machine-generated.

A new Full-Scale Guided Network (FSG-Net) enhances retinal vessel segmentation by refining features with an attention-guided filter. This compact model achieves state-of-the-art performance, improving fine vascular structure detection.

Keywords:

Comparative study Full-scale guided attention Guided filter Modified convolution block Retinal vessel Segmentation

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

The U-Net architecture has dominated retinal vessel segmentation for a decade.
Accurate segmentation of retinal vasculature is crucial for diagnosing eye diseases.

Purpose of the Study:

Introduce the Full-Scale Guided Network (FSG-Net) for improved retinal vessel segmentation.
Enhance feature representation and refinement for capturing fine vascular structures.

Main Methods:

Developed FSG-Net with a novel feature representation module using modernized convolution blocks.
Incorporated an attention-guided filter within a guided convolution block for structural refinement.
Evaluated FSG-Net against state-of-the-art methods on public datasets, ensuring reproducibility.

Main Results:

FSG-Net demonstrated competitive performance against state-of-the-art methods despite its compact architecture.
The attention-guided filter effectively enhanced fine vascular structures.
Ablation studies confirmed the significant contribution of each proposed component.

Conclusions:

FSG-Net offers a flexible and scalable approach to retinal vessel segmentation.
The proposed attention-guided mechanism significantly improves segmentation accuracy.
FSG-Net represents a promising advancement in automated retinal image analysis.