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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. Automated breast tumor segmentation methods have been proposed and can achieve promising results. However, these methods still need a pre-defined a region of interest (ROI) before performing segmentation, which makes them hard to run fully automatically. In this paper, we investigated automated localization and segmentation method for breast tumor in breast Dynamic Contrast-Enhanced MRI (DCE-MRI) scans. The proposed method takes advantage of kinetic prior and deep learning for automatic tumor localization and segmentation. We implemented our method and evaluated its performance on a dataset consisting of 74 breast MR images. We quantitatively evaluated the proposed method by comparing the segmentation with the manual annotation from an expert radiologist. Experimental results showed that the automated breast tumor segmentation method exhibits promising performance with an average Dice Coefficient of 0.89±0.06.
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