Detection　and　segmentation　of　the　hippocampal　structures　in　volumetric　brain　images　is　a　challenging　problem　in　the　area　of　medical　imaging.　In　this　paper,　we　propose　a　two-stage　3D　fully　convolutional　neural　network　that　efficiently　detects　and　segments　the　hippocampal　structures.　In　particular,　our　approach　first　localizes　the　hippocampus　from　the　whole　volumetric　image　and　obtains　a　rough　segmentation.　This　initial　segmentation　can　be　used　an　enhancement　mask　to　extract　the　fine　structure　of　the　hippocampus.　The　proposed　method　has　been　evaluated　on　a　public　dataset　and　compared　with　state-of-the-art　approaches.　Results　indicate　the　effectiveness　of　the　proposed　method,　which　yields　mean　Dice　Similarity　Coefficients　(i.e.　DSC)　of　0.897　and　0.900　for　the　left　and　right　hippocampus,　respectively.　Furthermore,　extensive　experiments　manifest　that　the　proposed　enhancement　mask　layer　has　remarkable　benefits　for　accelerating　training　process　and　obtaining　more　accurate　segmentation　results.　©　2018　IEEE.