Segmentation　of　breast　tumors　from　dynamic　contrast-enhanced　magnetic　resonance　(DCE-MR)　images　is　a　critical　step　in　the　diagnosis　of　breast　cancer　and　subsequent　efficacy　assessment.　However,　the　irregular　shape　and　size　of　breast　tumors,　as　well　as　the　inhomogeneity　of　the　background,　pose　challenges　to　accurately　segmenting　tumors　in　DCE-MR　images.　To　address　this,　our　study　proposes　a　breast　tumor　segmentation　model　based　on　U-net++　and　a　breast　region　localization　(BRL)　module　for　more　precise　segmentation　in　DCE-MRI.　This　model　helps　doctors　accurately　locate　the　tumor　position　and　perform　lesion　region　segmentation.　The　BRL　module　effectively　localizes　the　breast　region,　inhibits　confusion　of　tissues　in　the　chest　cavity,　and　accelerates　the　convergence　of　the　subsequent　segmentation　network.　U-Net++　applies　a　residual　network　structure　to　segment　the　lesion　region.　Experimental　results　show　that　our　proposed　segmentation　model　achieves　accurate　segmentation　of　breast　tumors,　with　a　Dice　coefficient　of　0.78　and　a　sensitivity　of　0.89.