  Retinal vessel segmentation plays an imaportant role in the field of retinal
image analysis because changes in retinal vascular structure can aid in the
diagnosis of diseases such as hypertension and diabetes. In recent research,
numerous successful segmentation methods for fundus images have been proposed.
But for other retinal imaging modalities, more research is needed to explore
vascular extraction. In this work, we propose an efficient method to segment
blood vessels in Scanning Laser Ophthalmoscopy (SLO) retinal images. Inspired
by U-Net, "feature map reuse" and residual learning, we propose a deep dense
residual network structure called DRNet. In DRNet, feature maps of previous
blocks are adaptively aggregated into subsequent layers as input, which not
only facilitates spatial reconstruction, but also learns more efficiently due
to more stable gradients. Furthermore, we introduce DropBlock to alleviate the
overfitting problem of the network. We train and test this model on the recent
SLO public dataset. The results show that our method achieves the
state-of-the-art performance even without data augmentation.
