  Accurate segmentation of the blood vessels of the retina is an important step
in clinical diagnosis of ophthalmic diseases. Many deep learning frameworks
have come up for retinal blood vessels segmentation tasks. However, the complex
vascular structure and uncertain pathological features make the blood vessel
segmentation still very challenging. A novel U-shaped network named
Multi-module Concatenation which is based on Atrous convolution and
multi-kernel pooling is put forward to retinal vessels segmentation in this
paper. The proposed network structure retains three layers the essential
structure of U-Net, in which the atrous convolution combining the multi-kernel
pooling blocks are designed to obtain more contextual information. The spatial
attention module is concatenated with dense atrous convolution module and
multi-kernel pooling module to form a multi-module concatenation. And different
dilation rates are selected by cascading to acquire a larger receptive field in
atrous convolution. Adequate comparative experiments are conducted on these
public retinal datasets: DRIVE, STARE and CHASE_DB1. The results show that the
proposed method is effective, especially for microvessels. The code will be put
out at https://github.com/Rebeccala/MC-UNet
