Tumor　segmentation　is　particularly　important　for　ultrasound　imaging-based　diagnosis　and　therapy,　such　as　breast　cancer　and　gastrointestinal　stromal　tumor.　However,　the　accurate　ultrasound　tumor　segmentation　remains　challenging　due　to　insufficient　textures　and　edge　features　resulted　from　limited　resolution,　low　signal-to-noise　ratio　(SNR),　and　artifacts　in　ultrasound　images.　To　address　the　challenges,　this　paper　proposes　an　novel　tumor　segmentation　network　that　combines　a　multi-scale　compression　attention　module　(MCAM)　and　an　edge　detection　module　(EDM).　MCAM　fuses　multi-scale　features　from　a　U-shaped　backbone　to　capture　global　semantic　features　using　a　compression　attention　mechanism.　EDM　introduces　multiple　convolutional　layers　to　extract　the　edge　features　of　tumor.　Furthermore,　a　semantic　and　texture　fusion　(STF)　mechanism　followed　by　an　improved　deep　supervision　is　proposed　to　strengthen　the　network＇s　performance　in　resolving　tumors.　Experimental　validations　on　a　public　dataset　and　a　private　dataset　demonstrated　the　effectiveness　of　the　proposed　modules　and　the　outperformance　of　our　network　over　the　current　advanced　segmentation　networks　in　various　metrics.　©　2025　IEEE.