  Accurate retinal vessel segmentation is a challenging problem in color fundus
image analysis. An automatic retinal vessel segmentation system can effectively
facilitate clinical diagnosis and ophthalmological research. Technically, this
problem suffers from various degrees of vessel thickness, perception of
details, and contextual feature fusion. For addressing these challenges, a deep
learning based method has been proposed and several customized modules have
been integrated into the well-known encoder-decoder architecture U-net, which
is mainly employed in medical image segmentation. Structurally, cascaded
dilated convolutional modules have been integrated into the intermediate
layers, for obtaining larger receptive field and generating denser encoded
feature maps. Also, the advantages of the pyramid module with spatial
continuity have been taken, for multi-thickness perception, detail refinement,
and contextual feature fusion. Additionally, the effectiveness of different
normalization approaches has been discussed in network training for different
datasets with specific properties. Experimentally, sufficient comparative
experiments have been enforced on three retinal vessel segmentation datasets,
DRIVE, CHASEDB1, and the unhealthy dataset STARE. As a result, the proposed
method outperforms the work of predecessors and achieves state-of-the-art
performance in Sensitivity/Recall, F1-score and MCC.
