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October 13, 2025Open Access

Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling

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Authors

JFJonathan FhimaJEJan Van EijgenLBLennert Beeckmans

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Overview

The proposed framework improves retinal vessel segmentation generalization using layout-aware image generation, indicating greater structural fidelity.

Key Points

  • RLAD enables better generalization in retinal vessel segmentation, increasing performance by up to 8.1%.
  • The method synthesizes retinal images and vessel segmentations driven by real layout components for diversity.
  • A comprehensive dataset of 586 segmented retinal images supports training and validation of the proposed methods.
  • Access to code and the dataset promotes reproducibility and encourages innovation in the field of medical imaging.

Cite This Study

Fhima et al. (2025) studied this question.

synapsesocial.com/papers/68ece2abd1bb2827d12971d4https://doi.org/10.48550/arxiv.2503.01190
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