# Few-shot annotation correction for lightweight retinal vessel image segmentation

## TL;DR

This paper introduces a lightweight method to correct errors in retinal vessel annotations, improving segmentation accuracy despite noisy labels.

## Contribution

A novel few-shot U-Net-based framework for annotation correction and noise robust learning in retinal vessel segmentation.

## Key findings

- Performance degrades as label displacement increases, as shown on the DRIVE dataset.
- The method achieves high accuracy (96.51-97.54) and AUC (98.01-98.45) on CHASE_DB1 and STARE.
- It demonstrates robustness to noisy labels and competitive performance against state-of-the-art methods.

## 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 an Accuracy of 96.51, an AUC of 98.01, and an F1 of 83.55 on CHASE_DB1 and an Accuracy of 97.54, an AUC of 98.45, and an 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.