Concerning the characteristics of breast cancer in Magnetic Resonance Imaging （MRI）， such as different shapes and sizes， and fuzzy boundaries， an algorithm based on multiscale residual U Network （UNet） with attention mechanism was proposed in order to avoid error segmentation and improve segmentation accuracy. Firstly， the multiscale residual units were used to replace two adjacent convolution blocks in the down-sampling process of UNet， so that the network could pay more attention to the difference of shape and size. Then， in the up-sampling stage， layer-crossed attention was used to guide the network to focus on the key regions， avoiding the error segmentation of healthy tissues. Finally， in order to enhance the ability of representing the lesions， the atrous spatial pyramid pooling was introduced as a bridging module to the network. Compared with UNet， the proposed algorithm improved the Dice coefficient， Intersection over Union （IoU）， SPecificity （SP） and ACCuracy （ACC） by 2.26， 2.11， 4.16 and 0.05 percentage points， respectively. The experimental results show that the algorithm can improve the segmentation accuracy of lesions and effectively reduce the false positive rate of imaging diagnosis.