Background/Objectives:　The　morphological　characteristics　of　breast　tumors　play　a　crucial　role　in　the　preliminary　diagnosis　of　breast　cancer.　However,　malignant　tumors　often　exhibit　rough,　irregular　edges　and　unclear,　boundaries　in　ultrasound　images.　Additionally,　variations　in　tumor　size,　location,　and　shape　further　complicate　the　accurate　segmentation　of　breast　tumors　from　ultrasound　images.　Methods:　For　these　difficulties,　this　paper　introduces　a　breast　ultrasound　tumor　segmentation　network　comprising　a　multi-scale　feature　acquisition　(MFA)　module　and　a　multi-input　edge　supplement　(MES)　module.　The　MFA　module　effectively　incorporates　dilated　convolutions　of　various　sizes　in　a　serial-parallel　fashion　to　capture　tumor　features　at　diverse　scales.　Then,　the　MES　module　is　employed　to　enhance　the　output　of　each　decoder　layer　by　supplementing　edge　information.　This　process　aims　to　improve　the　overall　integrity　of　tumor　boundaries,　contributing　to　more　refined　segmentation　results.　Results:　The　mean　Dice　(mDice),　Pixel　Accuracy　(PA),　Intersection　over　Union　(IoU),　Recall,　and　Hausdorff　Distance　(HD)　of　this　method　for　the　publicly　available　breast　ultrasound　image　(BUSI)　dataset　were　79.43%,　96.84%,　83.00%,　87.17%,　and　19.71　mm,　respectively,　and　for　the　dataset　of　Fujian　Cancer　Hospital,　90.45%,　97.55%,　90.08%,　93.72%,　and　11.02　mm,　respectively.　In　the　BUSI　dataset,　compared　to　the　original　UNet,　the　Dice　for　malignant　tumors　increased　by　14.59%,　and　the　HD　decreased　by　17.13　mm.　Conclusions:　Our　method　is　capable　of　accurately　segmenting　breast　tumor　ultrasound　images,　which　provides　very　valuable　edge　information　for　subsequent　diagnosis　of　breast　cancer.　The　experimental　results　show　that　our　method　has　made　substantial　progress　in　improving　accuracy.