Breast density(BD) is a significant risk factor for breast cancer and serves as a biomarker of risk in clinical trials. Breast segmentation is the first and an important step for accurate and reproducible BD estimation. However, the conventional manual segmentation is labor-intensive and bias-prone. Based on fat-water decomposition MRI, we developed an automated breast segmentation method and validated it against manual segmentation using 50 test-retest scans. The BD measures using our automated segmentation were very comparable to results from manual segmentation, and exhibited extremely high test-retest reproducibility. Our automated segmentation yielded more reproducible BD measures than the manual segmentation method.