Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

STAGE challenge: Structural-Functional Transition in Glaucoma Assessment.

Medical image analysis·2026

Same author

Host-Guest Recognition-Driven Colorimetric/Fluorescent Nanosensor Enables Ultrasensitive Hazardous Dodine Detection With Versatile Applications.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Systematic Abductive Reasoning via Diverse Relation Representations in Vector-Symbolic Architecture.

IEEE transactions on neural networks and learning systems·2026

Same author

Medial retropharyngeal nodal region sparing radiotherapy in nasopharyngeal carcinoma: five year analysis of open label, non-inferiority, multicentre, randomised phase 3 trial.

BMJ (Clinical research ed.)·2026

Same author

Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos.

Medical image analysis·2026

Same author

DNA damage-driven cGAS-STING activation via a nuclear-targeted probe enables potent near-infrared theranostics in breast cancer.

Acta biomaterialia·2026

Search research articles

Related Experiment Video

Updated: Jul 31, 2025

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

A lightweight network guided with differential matched filtering for retinal vessel segmentation.

Yubo Tan¹, Shi-Xuan Zhao¹, Kai-Fu Yang¹

¹The MOE Key Laboratory for Neuroinformation, Radiation Oncology Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, China.

Computers in Biology and Medicine

|May 5, 2023

Summary

This study introduces a new deep learning model for precise retinal vessel segmentation in fundus images, improving detection of thin vessels and reducing errors in challenging areas.

Keywords:

Anisotropic attention Fundus image Matched filtering Vessel segmentation

More Related Videos

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images

Published on: May 19, 2023

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

Related Experiment Videos

Last Updated: Jul 31, 2025

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images

Measuring Retinal Vessel Diameter from Mouse Fluorescent Angiography Images

Published on: May 19, 2023

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

Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Retinal vessel morphology indicates cardiovascular health, making fundus image analysis crucial.
Automated retinal vessel segmentation faces challenges with thin vessels, lesions, and low contrast.

Purpose of the Study:

To develop an advanced deep learning model for accurate thin vessel segmentation in fundus images.
To address limitations of existing methods regarding vessel breakage and false positives in complex regions.

Main Methods:

Proposed a novel network, differential matched filtering guided attention UNet (DMF-AU).
Incorporated differential matched filtering for initial vessel identification, feature anisotropic attention, and a multiscale consistency constrained backbone.
Utilized these components to enhance learning of vascular details and spatial linearity.

Main Results:

The DMF-AU model demonstrated superior performance in thin vessel segmentation compared to existing algorithms.
Achieved high accuracy on specially designed criteria for vessel segmentation tasks.
The model proved effective in handling areas with lesions and low contrast.

Conclusions:

DMF-AU is a high-performance, lightweight model for precise retinal vessel segmentation.
The proposed architecture effectively addresses challenges in segmenting thin vessels and reduces false positives.
This work offers a valuable tool for ophthalmologists analyzing fundus images for cardiovascular health assessment.