Despite the recent advances of deep learning algorithms in medical imaging, automatic segmentation algorithms for kidneys in Magnetic Resonance Imaging (MRI) examinations are lacking. Automated segmentation of kidneys in MRI can enable several clinical applications and use of radiomics and machine learning analysis of renal disease. In this work, we propose the application of a Mask R-CNN for the automatic segmentation of the kidneys in coronal T2- weighted single-shot fast spin echo MRI. We propose the morphological operations as post-processing to further improve the performance of Mask R-CNN for this task. With 5-fold cross-validation data, the proposed Mask R-CNN was trained and validated on 70 and 10 MRI exams, respectively, and then evaluated on the remaining 20 exams in each fold. Our proposed method achieved a dice score of 0.905 and Intersection over Union of 0.828.