VascX Models: Deep Ensembles for Retinal Vascular Analysis From Color Fundus Images.

ABSTRACT:

Purpose

To present and validate deep learning model ensembles (VascX) for vessel, artery-vein, optic disc segmentation, and fovea localization for color fundus images (CFIs). VascX preprocessing and inference code and model weights were made publicly available to facilitate research on retinal vasculature.

Methods

For model training, we combined over 15 published annotated datasets with CFIs from Dutch studies (mainly the Rotterdam Study). This resulted in diverse development sets with a variety of patient characteristics and imaging conditions. We trained UNet model ensembles using a new, more robust preprocessing algorithm and strong data augmentations. We compared VascX segmentation performance (Dice) to models with publicly available weights: AutoMorph and LittleWNet. We

SUBMITTER: Vargas Quiros J 

PROVIDER: S-EPMC12306690 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature