The　intensive　application　of　deep　learning　in　medical　image　processing　has　facilitated　the　advancement　of　automatic　retinal　vessel　segmentation　research.To　overcome　the　limitation　that　traditional　U-shaped　vessel　segmentation　networks　fail　to　extract　features　in　fundus　image　suffi-ciently,we　propose　a　novel　network(DSeU-net)based　on　deformable　convolution　and　squeeze　exci-tation　residual　module.The　deformable　convolution　is　utilized　to　dynamically　adjust　the　receptive　field　for　the　feature　extraction　of　retinal　vessel.And　the　squeeze　excitation　residual　module　is　used　to　scale　the　weights　of　the　low-level　features　so　that　the　network　learns　the　complex　relationships　of　the　different　feature　layers　efficiently.We　validate　the　DSeU-net　on　three　public　retinal　vessel　seg-mentation　datasets　including　DRIVE,CHASEDB1,and　STARE,and　the　experimental　results　demonstrate　the　satisfactory　segmentation　performance　of　the　network.