Papers › RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel...
RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network
Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod, Kenton M. Sanders, Salah A. Baker
High fidelity segmentation of both macro and microvascular structure of the retina plays a pivotal role in determining degenerative retinal diseases, yet it is a difficult problem. Due to successive resolution loss in the encoding phase combined with the inability to recover this lost information in the decoding phase, autoencoding based segmentation approaches are limited in their ability to extract retinal microvascular structure. We propose RV-GAN, a new multi-scale generative architecture for accurate retinal vessel segmentation to alleviate this. The proposed architecture uses two generators and two multi-scale autoencoding discriminators for better microvessel localization and segmentation. In order to avoid the loss of fidelity suffered by traditional GAN-based segmentation systems, we introduce a novel weighted feature matching loss. This new loss incorporates and prioritizes features from the discriminator's decoder over the encoder. Doing so combined with the fact that the discriminator's decoder attempts to determine real or fake images at the pixel level better preserves macro and microvascular structure. By combining reconstruction and weighted feature matching loss, the proposed architecture achieves an area under the curve (AUC) of 0.9887, 0.9914, and 0.9887 in pixel-wise segmentation of retinal vasculature from three publicly available datasets, namely DRIVE, CHASE-DB1, and STARE, respectively. Additionally, RV-GAN outperforms other architectures in two additional relevant metrics, mean intersection-over-union (Mean-IOU) and structural similarity measure (SSIM).
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | AUC | 0.9914 | #3 of 16 | Archive leaderboard | report |
| Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | F1 score | 0.8957 | #3 of 16 | Archive leaderboard | report |
| Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | Sensitivity | 0.8199 | #3 of 16 | Archive leaderboard | report |
| Retinal Vessel Segmentation | CHASE_DB1 | RV-GAN | mIOU | 0.9705 | #3 of 16 | Archive leaderboard | report |
| Retinal Vessel Segmentation | STARE | RV-GAN | AUC | 0.9887 | #4 of 10 | Archive leaderboard | report |
| Retinal Vessel Segmentation | STARE | RV-GAN | F1 score | 0.8323 | #4 of 10 | Archive leaderboard | report |
| Retinal Vessel Segmentation | STARE | RV-GAN | mIOU | 0.9754 | #4 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections