This paper proposes an improved dense residual U-shaped network (DRU-Net) to address the issues of blurry small vessel pixels and vessel discontinuity in retinal vessel segmentation.Firstly,the dense residual block (DRB) is proposed by combining the advantages of residual structure and dense connection,which is used to construct the encoding and decoding layers of the DRU-Net to fully extract the target features.Then,a multi-characteristic distillation module (MCDB) is added to the bottom of the network,which is built with dilated convolutions to extract image features at different scales.Finally,a bidirectional convolutional long short-term memory module (BConv LSTM) is introduced at the skip connection to fully fuse the shallow and deep features,and output the complete vessel map.Experimental results on the public datasets DRIVE and CHASE_DB1 achieve an accuracy of 0.966 9 and 0.976 4,respectively.Meanwhile,the area under curve (AUC) reaches 0.983 9 and 0.986 7,respectively,which demonstrates that the network has good segmentation performance and certain application value.