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Updated: Jan 10, 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

Improving retinal vessel assessment precision by integrating deep learning with interactive editing and graphical

Sojung Go¹, Jaemin Chae¹, Uichan Kim²

¹Department of Ophthalmology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.

Scientific Reports

|November 25, 2025

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We developed the SEoul Retinal Vessel Assessment Library (SERVAL), a software for precise retinal vessel measurement. SERVAL enhances quantitative analysis of fundus images for improved disease diagnosis.

Area of Science:

Ophthalmology
Medical Imaging
Biomedical Engineering

Background:

Accurate quantitative assessment of retinal vasculature is crucial for diagnosing and monitoring various retinal and systemic diseases.
Existing tools often lack the precision and comprehensive analysis capabilities required for detailed vascular characterization.

Purpose of the Study:

To introduce the SEoul Retinal Vessel Assessment Library (SERVAL), a novel software platform for precise quantitative measurement of vascular structures in fundus images.
To demonstrate SERVAL's superior performance in terms of precision, detail, consistency, and clinical practicality compared to existing methods.

Main Methods:

SERVAL integrates deep learning for automatic artery and vein mask initialization.
It employs subpixel refinement for vessel centerlines and boundaries, coupled with interactive editing tools.

Keywords:

Deep learning Fundus images Graphic user interface Retina Vessel measurement

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The platform enables accurate computation of a wide range of vessel assessment metrics from refined delineations.

Main Results:

Comparative analyses show SERVAL offers superior precision and structural detail over existing platforms.
Longitudinal studies confirm the measurement consistency of SERVAL.
Usability studies validate its clinical practicality for researchers and clinicians.

Conclusions:

SERVAL provides a user-friendly, precise, and reliable platform for quantitative retinal vessel analysis.
It is expected to significantly aid clinical research in developing novel vascular biomarkers and diagnostic metrics.
The software facilitates better characterization of complex vascular structures for improved disease management.