To　enhance　hepatic　vessel　structure　segmentation　in　the　absence　of　large　amount　of　hepatic　vessel　label　information,　this　paper　proposed　a　local-global-adjacent　information　fusion　module　and　embeded　it　into　the　encoding　phase　of　U-Net,　thus　obtaining　a　structure-aware　hepatic　vessel　segmentation　network.　The　module　has　three　branches,　including　residual　convolution　module　extracting　local　information　of　feature　maps,　long　distance　information　extraction　module　using　the　self-attention　mechanism　to　extract　global　information　of　feature　map　and　adjacent　information　extraction　module　using　adjacent　slices　to　supplement　contextual　information.　By　fusing　the　output　feature　maps　of　the　above　three　branch　modules,　the　vessel　structure　perception　capability　of　the　network　can　be　effectively　improved,　and　the　problems　of　the　2D　network　unable　to　represent　the　vessel’s　three-dimensional　direction　and　insufficient　training　data　of　the　3D　network　can　be　effectively　alleviated․　In　this　paper,　extensive　comparative　experiments　were　conducted　on　the　hepatic　vessel　and　tumor　datasets　in　the　Medical　Segmentation　Decathlon　dataset　and　the　hepatic　vessel　dataset　collected　and　labeled　by　us　in　a　third-class　hospital․　Results　show　that　our　method　achieves　the　optimal　performance　compared　with　several　mainstream　U-Net　segmentation　algorithms․　The　proposed　method’s　Dice　reaches　64․　04%　on　the　MICCAI　dataset　and　72․　07%　on　the　dataset　gathered　by　us.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.