Convolutional　neural　network　models　have　become　one　of　the　most　commonly　used　methods　for　analyzing　medical　images.　Among　them,　the　codec　structure　has　brought　important　breakthrough　results　for　medical　image　segmentation.　However,　the　current　medical　image　segmentation　method　based　on　the　codec　network　architecture　still　has　many　problems.　The　corresponding　feature　map　of　the　codec　network　in　the　skip　connection　structure　has　a　large　semantic　ambiguity,　which　may　increase　the　difficulty　of　learning　the　network　and　reduce　the　segmentation　performance.　The　codec　network　architecture　cannot　make　full　use　of　the　relationship　between　objects　in　the　global　view,　and　also　ignores　the　global　context　information　of　different　scales.　In　this　article,　we　add　attention　gate　mechanism　(AGs)　to　the　jump　connection　structure,　and　introduce　attention　mechanism　and　multi-scale　mechanism　to　solve　the　above　problems.　Our　model　obtains　better　segmentation　performance　while　introducing　fewer　parameters.