The　automatic　segmentation　of　retinal　vessels　plays　an　important　role　in　the　early　screening　of　eye　diseases.　However,　vessels　are　difficult　to　segment　with　pathological　retinal　images.　Hence,　we　propose　the　use　of　deep　U-net,　a　new　retinal　vessel　segmentation　method　based　on　an　improved　U-shaped　fully　convolutional　neural　network.　The　method　uses　not　only　local　features　learned　from　the　shallow　convolution　layers,　but　also　abstract　features　learned　from　deep　convolution　layers.　To　improve　the　segmentation　accuracy　for　thin　vessels,　we　applied　Gaussian　matched　filtering　to　the　U-net.　The　batch　normalization　layer　was　added　in　the　U-net　network,　which　increased　the　speed　of　convergence.　In　the　training　phase,　a　new　sample　amplification　method　called　translation-reflection　was　proposed　to　increase　the　proportion　of　blood　vessels　in　the　training　images.　Results　of　the　experiments　showed　that　the　proposed　method　leads　to　better　retinal　vessel　segmentation　than　other　methods　developed　in　recent　years　do　for　the　SE,　SP,　Acc,　Ppv,　and　AUC　evaluation　metrics.