Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling
Paper • 2503.01190 • Published
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling
State-of-the-art AV segmentation in retinal fundus images
Jonathan Fhima • Jan Van Eijgen • Lennert Beeckmans • Thomas Jacob • Moti Freiman
Luis Filipe Nakayama • Ingeborg Stalmans • Chaim Baskin • Joachim A. Behar
The strongest open-weight model for retinal artery/vein segmentation we know of, trained with RLAD-generated images and the REYIA dataset. Internally also known as LUNetV2.
Quickstart
python3 -m venv lunetv2_env && source lunetv2_env/bin/activate
pip install -r requirements.txt
python infer.py fundus.png out/
Writes out/mask.png (arteries red, veins blue) and out/overlay.png
(blended with the input). Runs in bfloat16, the precision the model was trained in.
For a full pipeline from a raw fundus image to vasculature biomarkers, see the RLAD + PVBM end-to-end tutorial, which feeds this model's output into PVBM.
Example
Results
If you plan to use this model or compare against it, we recommend
scoring against our benchmark rather than re-running every baseline
yourself. The scorer that produced these numbers lives in the
REYIA repo
(scripts/eval/dice_score.py).
Artery/vein Dice score, averaged across the same local, external, and out-of-distribution (OOD) test sets; ∆ rows show the improvement RLAD adds on top of each backbone, and † marks results with data leakage during training — this release is the SwinV2-large row.
Ablations
License
The code (LUNetv2.py, infer.py) is Apache 2.0. The weights
(weights/model.safetensors) are non-commercial, research use only — the
training set mixes in subsets that are themselves non-commercial or
restricted:
| Source | Status |
|---|---|
| UZLF | KU Leuven dataset |
| GRAPE | CC0 1.0 — see REYIA |
| MAGRABIA | CC BY-NC 4.0 — see REYIA |
| MESSIDOR | redistribution and commercial use explicitly prohibited — see REYIA |
| PAPILA | figshare license field reads GPL 3.0+ — likely a depositor mis-click, not corrected as of this writing — see REYIA |
| ENRICH | non-commercial use only, KU Leuven data-sharing protocol — see REYIA |
Citation
Fhima, J., Van Eijgen, J., Beeckmans, L., Jacob, T., Freiman, M., Nakayama, L.F., Stalmans, I., Baskin, C., Behar, J.A. Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling. arXiv:2503.01190, 2025. https://arxiv.org/abs/2503.01190