Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling
Journal:
arXiv
Published Date:
Mar 3, 2025
Abstract
Generalization in medical segmentation models is challenging due to limited
annotated datasets and imaging variability. To address this, we propose Retinal
Layout-Aware Diffusion (RLAD), a novel diffusion-based framework for generating
controllable layout-aware images. RLAD conditions image generation on multiple
key layout components extracted from real images, ensuring high structural
fidelity while enabling diversity in other components. Applied to retinal
fundus imaging, we augmented the training datasets by synthesizing paired
retinal images and vessel segmentations conditioned on extracted blood vessels
from real images, while varying other layout components such as lesions and the
optic disc. Experiments demonstrated that RLAD-generated data improved
generalization in retinal vessel segmentation by up to 8.1%. Furthermore, we
present REYIA, a comprehensive dataset comprising 586 manually segmented
retinal images. To foster reproducibility and drive innovation, both our code
and dataset will be made publicly accessible.
