  Despite the recent advances of deep learning algorithms in medical imaging,
the automatic segmentation algorithms for kidneys in MRI exams are still
scarce. Automated segmentation of kidneys in Magnetic Resonance Imaging (MRI)
exams are important for enabling radiomics and machine learning analysis of
renal disease. In this work, we propose to use the popular Mask R-CNN for the
automatic segmentation of kidneys in coronal T2-weighted Fast Spin Eco slices
of 100 MRI exams. 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 is trained and validated on 70
and 10 MRI exams and then evaluated on the remaining 20 exams in each fold. Our
proposed method achieved a dice score of 0.904 and IoU of 0.822.
