Retinal　vessel　segmentation　is　a　significant　problem　in　the　analysis　of　fundus　images.　A　novel　deep　learning　structure　called　the　Gaussian　net　(GNET)　model　combined　with　a　saliency　model　is　proposed　for　retinal　vessel　segmentation.　A　saliency　image　is　used　as　the　input　of　the　GNET　model　replacing　the　original　image.　The　GNET　model　adopts　a　bilaterally　symmetrical　structure.　In　the　left　structure,　the　first　layer　is　upsampling　and　the　other　layers　are　max-pooling.　In　the　right　structure,　the　final　layer　is　max-pooling　and　the　other　layers　are　upsampling.　The　proposed　approach　is　evaluated　using　the　DRIVE　database.　Experimental　results　indicate　that　the　GNET　model　can　obtain　more　precise　features　and　subtle　details　than　the　UNET　models.　The　proposed　algorithm　performs　well　in　extracting　vessel　networks,　and　is　more　accurate　than　other　deep　learning　methods.　Retinal　vessel　segmentation　can　help　extract　vessel　change　characteristics　and　provide　a　basis　for　screening　the　cerebrovascular　diseases.