In　the　medical　field,　abdominal　ultrasound　examination　can　observe　abdominal　organs　and　lesions　in　real　time　and　non-invasively,　precisely　dividing　ultrasonic　images　is　challenging　due　to　low　contrast　and　sound　interference.　This　paper　proposes　a　new　network　that　combines　convolution　and　self-attention　mechanisms　based　on　deep　learning.　It　learns　global　dependencies　by　introducing　a　transformer,　and　uses　a　cross-channel　attention　(multi-head　cross-attention)　mechanism　for　multi-scale　feature　fusion.　By　introducing　the　transformer,　the　network　can　effectively　learn　the　global　features　of　the　image　and　establish　global　dependencies.　Multi-head　cross-attention　can　make　full　use　of　the　semantic　information　of　the　encoder　and　the　high-resolution　features　of　the　decoder　to　achieve　more　accurate　feature　fusion　and　positioning.　The　method　was　verified　on　the　public　abdominal　ultrasound　data　set.　Compared　with　the　U-Net　network,　the　IoU　and　Dice　coefficients　were　improved,　showing　a　more　accurate　segmentation　effect.　This　work　shows　that　this　method　can　significantly　improve　the　performance　of　ultrasound　image　segmentation　and　is　valuable　for　ultrasound　medical　image　analysis.　©　2023　IEEE.