Automatic　optic　disk　(OD)　segmentation　is　an　important　tool　for　early　detection　of　eye　diseases.　In　this　article,　we　proposed　a　Res-UNet　network　by　applying　residual　learning　module　and　other　improvements　in　U-Net　for　optic　disk　segmentation　in　retinal　image.　Since　training　data　available　is　insufficient,　we　enlarge　the　data　set　by　generating　data　pieces.　Res-UNet　is　then　trained　to　classify　each　pixel　of　the　input　retinal　image.　Finally,　the　predicted　probability　map　is　further　post-processed　with　morphological　technique　to　get　final　OD　segmentation　result.　Experiments　on　the　public　DRISHTI-GS　data　set　including　comparison　with　the　best　known　methods　show　that　the　proposed　model　outperforms　most　existing　methods　on　several　metrics.　©　2020　Computer　Society　of　the　Republic　of　China.　All　rights　reserved.