  The detection of retinal vessel is of great importance in the diagnosis and
treatment of many ocular diseases. Many methods have been proposed for vessel
detection. However, most of the algorithms neglect the connectivity of the
vessels, which plays an important role in the diagnosis. In this paper, we
propose a novel method for retinal vessel detection. The proposed method
includes a dense dilated network to get an initial detection of the vessels and
a probability regularized walk algorithm to address the fracture issue in the
initial detection. The dense dilated network integrates newly proposed dense
dilated feature extraction blocks into an encoder-decoder structure to extract
and accumulate features at different scales. A multiscale Dice loss function is
adopted to train the network. To improve the connectivity of the segmented
vessels, we also introduce a probability regularized walk algorithm to connect
the broken vessels. The proposed method has been applied on three public data
sets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method
outperforms the state-of-the-art methods in accuracy, sensitivity, specificity
and also are under receiver operating characteristic curve.
