ORIGINAL RESEARCH article
Front. Med.
Sec. Ophthalmology
Few-Shot Annotation Correction for Lightweight Retinal Vessel Image Segmentation
Huazhang Li 1,2
Yueming Sun 2
Daniel Organisciak 2
Yang Long 2
Ying Su 1
Feng Wang 1
1. Harbin Medical University, Harbin, China
2. Durham University, Durham, United Kingdom
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Abstract
ABSTRACT Retinal vessel segmentation underpins quantitative analysis in ophthalmology and is widely used for screening and diagnosis. In practice, manual annotations for thin and tortuous vessels are error prone, yet the effect of positional label noise on segmentation quality remains underexplored. We address this gap with a lightweight few shot U Net based framework for annotation correction and noise robust learning. Analyses on DRIVE reveal clear performance degradation as label displacement increases. Cross dataset validation shows that the proposed method attains Accuracy of 96.51, AUC of 98.01, F1 of 83.55 on CHASE DB1 and Accuracy of 97.54, AUC of 98.45, F1 of 83.11 on STARE, achieving competitive performance against state of the art methods. These results quantify the sensitivity of vessel segmentation to positional annotation errors and demonstrate practical robustness under noisy labels.
Summary
Keywords
deep learning, Medical Image Analysis, Noisy annotations, Retinal vessel segmentation, U-net
Received
09 August 2025
Accepted
29 January 2026
Copyright
© 2026 Li, Sun, Organisciak, Long, Su and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ying Su
Disclaimer
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