Magnetic resonance imaging (MRI) plays an important role in breast cancer detection and diagnosis. Breast region segmentation on MRI images is an essential step for many analysis tasks such as the assessment of background parenchymal enhancement (BPE), the analysis of non-mass enhancement (NME) and investigation of the tumor characteristics. However, automated breast segmentation remains a challenge because the noisy background and the blurred boundary between breast and pectoral muscle on MRI images. In this study, we developed a coordinate-guided U-Net (CU-Net) to identify the boundary of the breast on MRI images. The CU-Net applied the breast position information to the segmentation of breast by adding the X coordinate channel and Y coordinate channel to the input image. A dataset of 80 3D bilateral breast MRI scans including 64 cases in training/validation set and 16 cases in test set were collected. All the input images were resized to 256×256 pixels and the intensity are cropped and rescaled according to the percentile of cumulative histogram to normalize the image. To add X and Y coordinate channels, the middle point of the breast-air boundary was detected as the origin of coordinates. The position of each pixel in the coordinates was then encoded into the input of the network. The CU-Net was evaluated with three measures: the dice coefficient (Dice), the intersection over union (IoU) and the hausdorff distance (Hdist). The results of CU-Net were compared to those obtained with U-Net method. In the test set including 16 cases, the mean Dice, the mean IoU, and the mean Hdist of CUNet were 94.1±1.1%, 92.5±1.3%, and 6.17±1.4 mm, while the corresponding measures by U-Net were 91.4±2.4%, 89.7±1.7%, and 6.98±1.5 mm, respectively. The results showed that our CU-Net was able to segment breast on MRI images reliably and was more accurate comparing to the U-Net.