  Accurate segmentation of retinal vessels is a basic step in Diabetic
retinopathy(DR) detection. Most methods based on deep convolutional neural
network (DCNN) have small receptive fields, and hence they are unable to
capture global context information of larger regions, with difficult to
identify lesions. The final segmented retina vessels contain more noise with
low classification accuracy. Therefore, in this paper, we propose a DCNN
structure named as D-Net. In the proposed D-Net, the dilation convolution is
used in the backbone network to obtain a larger receptive field without losing
spatial resolution, so as to reduce the loss of feature information and to
reduce the difficulty of tiny thin vessels segmentation. The large receptive
field can better distinguished between the lesion area and the blood vessel
area. In the proposed Multi-Scale Information Fusion module (MSIF), parallel
convolution layers with different dilation rates are used, so that the model
can obtain more dense feature information and better capture retinal vessel
information of different sizes. In the decoding module, the skip layer
connection is used to propagate context information to higher resolution
layers, so as to prevent low-level information from passing the entire network
structure. Finally, our method was verified on DRIVE, STARE and CHASE dataset.
The experimental results show that our network structure outperforms some
state-of-art method, such as N4-fields, U-Net, and DRIU in terms of accuracy,
sensitivity, specificity, and AUCROC. Particularly, D-Net outperforms U-Net by
1.04%, 1.23% and 2.79% in DRIVE, STARE, and CHASE three dataset, respectively.
