Accurate　and　efficient　segmentation　of　multiple　abdominal　organs　from　medical　images　is　crucial　for　clinical　applications　such　as　disease　diagnosis　and　treatment　planning.　In　this　paper,　we　propose　a　novel　approach　for　abdominal　organ　segmentation　using　the　U-Net　architecture.　Our　method　addresses　the　challenges　posed　by　anatomical　variations　and　the　proximity　of　organs　in　the　abdominal　region.　To　improve　the　segmentation　accuracy,　we　introduce　an　attention　mechanism　into　the　U-Net　architecture.　This　mechanism　allows　the　network　to　focus　on　salient　regions　and　suppress　irrelevant　background　regions,　enhancing　the　overall　segmentation　performance.　Additionally,　we　incorporate　3D　information　by　connecting　three　consecutive　slices　as　3-dimensional　inputs.　This　enables　us　to　exploit　the　spatial　context　across　the　slices　while　minimizing　the　increase　in　GPU　memory　usage.　We　evaluate　our　proposed　method　on　the　MICCAI　FLARE　2023　validation　dataset,　the　mean　DSC　is　0.3683　and　the　mean　NSD　is　0.3668.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2024.