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Retinal blood vessels segmentation acts as an important part to the treatment of ocular disease. Lately, automatic segmentation based on deep learning can solves problems of low efficiency and strong subjectivity of manual segmentation, and attracts the attention of researchers. In this paper, a novel segmentation model called Residualpath-Res-Dense Net (RRD-Net) is proposed to achieve vessel segmentation. In RRD-Net, Res-block and Dense-block are used to help speeding up the convergence of the network and learning more intrinsic features. In addition, the introduction of Residual Path can cut down the semantic difference between the connected feature and eliminate the potential impact of semantic difference on segmentation accuracy. We apply benchmark datasets DRIVE and CHASE_DB1 to evaluate effectiveness of the proposed network. Accuracy, sensitivity and F1 score demonstrate the effectiveness of RRD-Net.
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