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Updated: Jan 10, 2026
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.
Abstract:
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.
