Breast magnetic resonance imaging (MRI) plays an important role in high-risk breast cancer screening, clinical problemsolving, and imaging-based outcome prediction. Breast tumor segmentation in MRI is an essential step for quantitative radiomics analysis, where automated and accurate tumor segmentation is needed but very challenging. Manual tumor annotation by radiologists requires medical knowledge and is time-consuming, subjective, prone to error, and inter-user inconsistency. Several recent studies have shown the ability of deep-learning models in image segmentation. In this work, we investigated a deep-learning based method to segment breast tumors in Dynamic Contrast-Enhanced MRI (DCE-MRI) scans in both 2D and 3D settings. We implemented our method and evaluated its performance on a dataset of 1,246 breast MR images by comparing the segmentation to the manual annotations from expert radiologists. Experimental results showed that the deep-learning-based methods exhibit promising performance with the best Dice Coefficient of 0.92 ± 0.02.