  Existing supervised approaches didn't make use of the low-level features
which are actually effective to this task. And another deficiency is that they
didn't consider the relation between pixels, which means effective features are
not extracted. In this paper, we proposed a novel convolutional neural network
which make sufficient use of low-level features together with high-level
features and involves atrous convolution to get multi-scale features which
should be considered as effective features. Our model is tested on three
standard benchmarks - DRIVE, STARE, and CHASE databases. The results presents
that our model significantly outperforms existing approaches in terms of
accuracy, sensitivity, specificity, the area under the ROC curve and the
highest prediction speed. Our work provides evidence of the power of wide and
deep neural networks in retinal blood vessels segmentation task which could be
applied on other medical images tasks.
