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

Amplification of a metabolic shunt for elicitor detoxification: a jasmonate-exploited insect counter-defense strategy.

Pest management science·2026

Same author

From tumor regression grading to interpretable endpoints in neoadjuvant oncology.

NPJ precision oncology·2026

Same author

Antimicrobial resistance trends among dominant pathogens in six clinical departments of the Fourth Affiliated Hospital of Guangxi Medical University, 2020-2024.

Frontiers in public health·2026

Same author

Multistimuli-Controlled Topological Nucleation of Skyrmion Loops and Monopoles in Liquid Crystals.

Physical review letters·2026

Same author

Dynamic creation of topological solitons via nematic vortex lines.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Clinical Utility of Urinary Cystatin C in Early Screening and Staging of Diabetic Kidney Disease in Type 2 Diabetes.

International journal of endocrinology·2026

Search research articles

Related Experiment Video

Updated: Aug 17, 2025

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

CPMF-Net: Multi-Feature Network Based on Collaborative Patches for Retinal Vessel Segmentation.

Wentao Tang¹, Hongmin Deng¹, Shuangcai Yin¹

¹School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|December 11, 2022

Summary

This summary is machine-generated.

This study introduces a novel deep learning method for retinal blood vessel segmentation, improving accuracy for diagnosing eye diseases by using collaborative patches and multi-feature networks. The new approach enhances capillary segmentation, a challenging but crucial diagnostic indicator.

Keywords:

channel attention fundus image self-attention training methods vessel segmentation

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

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

Related Experiment Videos

Last Updated: Aug 17, 2025

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

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

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

Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Retinal vessel morphology is vital for early eye disease diagnosis.
Deep learning has advanced retinal blood vessel segmentation.
Segmenting capillaries remains challenging due to image complexity and quality.

Purpose of the Study:

To develop an accurate retinal blood vessel segmentation method.
To address challenges in segmenting complex vessel structures and capillaries.
To improve early diagnosis of eye diseases through enhanced segmentation.

Main Methods:

Proposed a multi-feature segmentation method based on collaborative patches.
Introduced a collaborative patch training strategy to compensate for pixel information loss.
Designed a multi-feature network incorporating adaptive coordinate attention and gated self-attention modules.

Main Results:

The proposed method achieved superior performance on the DRIVE and STARE datasets.
Outperformed nine other advanced retinal blood vessel segmentation methods.
Demonstrated effectiveness in segmenting challenging retinal structures, including capillaries.

Conclusions:

The multi-feature segmentation method based on collaborative patches offers a significant advancement in retinal blood vessel segmentation.
This technique holds promise for improving the accuracy and efficiency of diagnosing eye diseases.
The proposed approach effectively handles image complexity and enhances capillary segmentation.