  Automatic segmentation of retinal vessels in fundus images plays an important
role in the diagnosis of some diseases such as diabetes and hypertension. In
this paper, we propose Deformable U-Net (DUNet), which exploits the retinal
vessels' local features with a U-shape architecture, in an end to end manner
for retinal vessel segmentation. Inspired by the recently introduced deformable
convolutional networks, we integrate the deformable convolution into the
proposed network. The DUNet, with upsampling operators to increase the output
resolution, is designed to extract context information and enable precise
localization by combining low-level feature maps with high-level ones.
Furthermore, DUNet captures the retinal vessels at various shapes and scales by
adaptively adjusting the receptive fields according to vessels' scales and
shapes. Three public datasets DRIVE, STARE and CHASE_DB1 are used to train and
test our model. Detailed comparisons between the proposed network and the
deformable neural network, U-Net are provided in our study. Results show that
more detailed vessels are extracted by DUNet and it exhibits state-of-the-art
performance for retinal vessel segmentation with a global accuracy of
0.9697/0.9722/0.9724 and AUC of 0.9856/0.9868/0.9863 on DRIVE, STARE and
CHASE_DB1 respectively. Moreover, to show the generalization ability of the
DUNet, we used another two retinal vessel data sets, one is named WIDE and the
other is a synthetic data set with diverse styles, named SYNTHE, to
qualitatively and quantitatively analyzed and compared with other methods.
Results indicates that DUNet outperforms other state-of-the-arts.
