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 a deep learning method called Mask R-CNN for the segmentation of kidneys in 2D coronal T2W FSE images of 94 MRI exams. With 5-fold cross-validation data, the Mask R-CNN is trained and validated on 66 and 9 MRI exams and then evaluated on the remaining 19 exams. Our proposed method achieved an average dice score of 0.839 and an average IoU of 0.763.
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