  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).
