Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling

Jonathan Fhima, Jan Van Eijgen, Lennert Beeckmans, Thomas Jacobs, Moti Freiman, Luis Filipe Nakayama, Ingeborg Stalmans, Chaim Baskin, Joachim Behar

Keywords: Blood Vessel Segmentation, Synthetic Data Generation, Generalization in Medical Segmentation, Controllable Image Synthesis

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 585 manually segmented retinal images. To foster reproducibility and drive innovation, both our code and dataset will be made publicly accessible.

Dataset URL: https://kaggle.com/datasets/ba1b909c12dbe6c08df00b3ee6fc22d2fef632870359f91384b9001a870f67bf

Code URL: https://drive.google.com/file/d/17MOCSQzA4KWQQqYcEQLb-Uti8J-Cc3RL/view?usp=share_link

Primary Area: AL/ML Datasets & Benchmarks for health sciences (e.g. climate, health, life sciences, physics, social sciences)

Flagged For Ethics Review: true

Submission Number: 2483