Existing　neural　network　segmentation　schemes　perform　well　in　the　task　of　segmenting　images　of　organs　with　large　areas　and　clear　morphology,　such　as　the　liver　and　lungs.　However,　it　is　difficult　to　segment　organs　with　variable　morphology　and　small　target　area,　such　as　pancreas　and　tumors.　In　order　to　achieve　accurate　segmentation　of　pancreas　and　its　cysts,　MDAG-Net　(Multi-dimensional　Attention　Gate　Network)　is　proposed　in　this　paper.　Combining　three　attention　mechanisms:　spatial,　channel　and　multi-dimensional　feature　map　input,　MDAG(Multi-dimensional　Attention　Gate)　obtains　the　global　distribution　of　semantic　information　in　spatial　and　channel　dimensions,　filters　redundant　information　in　shallow　feature　maps,　realizes　feature　response,　and　recalibrates　convolution　kernel　parameters.　In　addition,　the　WML(Weighted　cross　entropy　and　MIoU　loss　function)　loss　can　adaptively　assign　the　weight　of　category　loss　and　count　the　classification　error　of　global　pixels,　which　can　increase　the　error　attention　of　the　target　area　and　improving　the　segmentation　accuracy　of　the　network.　The　algorithm　is　experimented　on　the　Task07_Pancreas　dataset,　compared　with　U-Net　under　the　same　conditions,　the　Dice　coefficient,　Precision,　Recall　rate　and　MIoU　(Mean　Intersection　over　Union)　of　MDAG-Net　are　improved　by　5.3%,　1.5%,　12.7%　and　7.6%　respectively.　The　results　show　that　MDAG-Net　can　accurately　segment　the　region　of　pancreas　and　its　cyst　in　CT(Computed　Tomography)　images,　which　proves　that　MDAG　has　better　segmentation　efficiency　for　such　small　target　regions.　©　2022　Elsevier　Ltd