Improving Retinal Vessel Assessment Precision by Integrating Deep Learning with Interactive Editing and Graphical Modeling

TY - JOUR

T1 - Improving Retinal Vessel Assessment Precision by Integrating Deep Learning with Interactive Editing and Graphical Modeling

AU - Go, Sojung

AU - Chae, Jaemin

AU - Kim, Uichan

AU - Lim, Jongsoo

AU - Kim, Jooyoung

AU - Hogg, Stephen

AU - Trucco, Emanuele

AU - Park, Sang Jun

AU - Lee, Soochahn

N1 - Copyright: © The Author(s) 2025.

PY - 2025/11/24

Y1 - 2025/11/24

N2 - We present the SEoul Retinal Vessel Assessment Library (SERVAL), a novel software platform for precise quantitative measurement of vascular structures in fundus images. SERVAL integrates deep learning-based automatic artery and vein mask initialization, subpixel vessel centerline and boundary refinement, and interactive editing tools within a user-friendly graphical interface. From the refined artery and vein delineations, it enables accurate computation of a wide range of vessel assessment metrics, facilitating better characterization of complex vascular structures. We evaluate SERVAL through: 1) comparative analyses with existing platforms, highlighting its superior precision and structural detail; 2) longitudinal image studies demonstrating measurement consistency; and 3) a usability study confirming its clinical practicality. We expect SERVAL to serve as a valuable tool in clinical research, supporting the development of novel vascular biomarkers and diagnostic metrics for retinal and systemic diseases.

AB - We present the SEoul Retinal Vessel Assessment Library (SERVAL), a novel software platform for precise quantitative measurement of vascular structures in fundus images. SERVAL integrates deep learning-based automatic artery and vein mask initialization, subpixel vessel centerline and boundary refinement, and interactive editing tools within a user-friendly graphical interface. From the refined artery and vein delineations, it enables accurate computation of a wide range of vessel assessment metrics, facilitating better characterization of complex vascular structures. We evaluate SERVAL through: 1) comparative analyses with existing platforms, highlighting its superior precision and structural detail; 2) longitudinal image studies demonstrating measurement consistency; and 3) a usability study confirming its clinical practicality. We expect SERVAL to serve as a valuable tool in clinical research, supporting the development of novel vascular biomarkers and diagnostic metrics for retinal and systemic diseases.

KW - Deep learning

KW - Fundus images

KW - Graphic user interface

KW - Retina

KW - Vessel measurement

UR - https://www.scopus.com/pages/publications/105022725730

U2 - 10.1038/s41598-025-25421-6

DO - 10.1038/s41598-025-25421-6

M3 - Article

C2 - 41285945

SN - 2045-2322

VL - 15

JO - Scientific Reports

JF - Scientific Reports

M1 - 41552

ER -