Hippocampus　is　one　of　the　first　affected　brain　regions　in　Alzheimer＇s　disease　(AD).　To　help　AD　diagnosis,　hippocampal　atrophy　have　been　studied　extensively,　but　accurate　segmentation　of　hippocampus　has　always　been　a　difficult　problem.　In　this　study,　we　propose　a　3D　multi-scale　multi-attention　UNet　with　the　use　of　multi-scale　inception　module,　residual　connections　and　attention　modules　for　automatic　hippocampal　segmentation.　Experimental　results　on　the　HarP　dataset　show　that　our　proposed　method　can　achieve　average　Dice　coefficient　of　0.827　for　hippocampal　segmentation,　which　outperform　classic　3D　UNet.　The　efficiency　and　accuracy　of　the　proposed　methods　suggests　that　it　may　facilitate　the　study　of　the　hippocampus　for　large-scale　studies.　©　2021　IEEE.