FPG-Seg: Annotation-free Retinal Vessel Topology Segmentation

James Willoughby; Irina Voiculescu

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arXiv · 2610.22259

FPG-Seg: Annotation-free Retinal Vessel Topology Segmentation

James Willoughby·Irina Voiculescu

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Abstract

State-of-the art approaches to retinal vessel segmentation typically require extensive annotations, a highly time-consuming process, reliant on clinical expertise. We present FPG-Seg, an annotation-free pipeline for retinal vessel segmentation combining classical vesselness filtering, Bayesian deep learning and topology-aware graph reconstruction to segment retinal vessel topology without manual labels. Tested across four widely used benchmark datasets for retinal vessel segmentation, FPG-Seg attains a clDice of $0.74{-}0.78$, representing $86{-}98\%$ of the performance of an equivalent fully-supervised baseline, despite using no manual annotations for training or hyperparameter tuning. As part of this work, we also provide, to our knowledge, the first performance benchmark of an annotation-free vessel segmentation method on FIVES.

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James Willoughby, Irina Voiculescu. 2026-10-15. FPG-Seg: Annotation-free Retinal Vessel Topology Segmentation. https://arxiv.org/abs/2610.22259

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