Breast cancer is one of the most common cancers in women. Early diagnosis using MRI can significantly increase the cure rate. However, breast tumor in early stage is small whose edge is ambiguous, leading to false detection and missing detection. To this end, we propose a shape enhanced U-Net with Transformer encoder layer (called SETE-UNet) for automatically segmenting the tumor from breast cancer. First, Transformer encoder layer is introduced to provide global self-attention mechanisms, achieving obvious promotion for tumor segmentation. Second, a discriminative representation is calculated by the proposed shape enhanced branch, which introduces a novel branch to learn a shape boundary with additional supervision. This proposed module can help our algorithm focus on the relevant boundary and also achieve further performance improvement for tiny tumor. Furthermore, we construct an MRI dataset of breast tumors in early stage for breast tumor segmentation, containing 260 cases, including 243 women and 17 men. We compare the proposed method with the popular algorithms on our dataset of breast cancer in early stage, which shows that our method presents excellent segmentation results on breast MRI containing various organs with respect to MIoU, Precision, Accuracy, Dice, Sensitivity, Specificity, AUC, Hausdorff Distance.