Code released for SERVAL (Scientific Reports 2025 paper)

We have released an initial version of code for our Scientific Reports 2025 paper on interactive segmentation of vascular structures.

Title: Improving retinal vessel assessment precision by integrating deep learning with interactive editing and graphical modeling Authors: Sojung Go, Jaemin Chae, Uichan Kim, Jongsoo Lim, Jooyoung Kim, Sang Jun Park, Soochahn Lee Code: github.com/snubhretina/SERVAL Paper: Scientific Reports article

The released SERVAL (SEoul Retinal Vessel Assessment Library) code provides the GUI tool together with the deep learning vessel segmentation and artery/vein classification modules, the interactive semi-automatic editing operations for vessel connection and A/V label correction, and the graphical modeling step that enforces topological consistency of the vessel tree. We hope this will support the community in building on our work.