  Retinal vessel segmentation contributes significantly to the domain of
retinal image analysis for the diagnosis of vision-threatening diseases. With
existing techniques the generated segmentation result deteriorates when
thresholded with higher confidence value. To alleviate from this, we propose
RVGAN, a new multi-scale generative architecture for accurate retinal vessel
segmentation. Our architecture uses two generators and two multi-scale
autoencoder based discriminators, for better microvessel localization and
segmentation. By combining reconstruction and weighted feature matching loss,
our adversarial training scheme generates highly accurate pixel-wise
segmentation of retinal vessels with threshold >= 0.5. The architecture
achieves AUC of 0.9887, 0.9814, and 0.9887 on three publicly available
datasets, namely DRIVE, CHASE-DB1, and STARE, respectively. Additionally,
RV-GAN outperforms other architectures in two additional relevant metrics,
Mean-IOU and SSIM.
