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Despite the recent advances of deep learning algorithms in medical imaging,the automatic segmentation algorithms for kidneys in MRI exams are stillscarce. Automated segmentation of kidneys in Magnetic Resonance Imaging (MRI)exams are important for enabling radiomics and machine learning analysis ofrenal disease. In this work, we propose to use the popular Mask R-CNN for theautomatic segmentation of kidneys in coronal T2-weighted Fast Spin Eco slicesof 100 MRI exams. We propose the morphological operations as post-processing tofurther improve the performance of Mask R-CNN for this task. With 5-foldcross-validation data, the proposed Mask R-CNN is trained and validated on 70and 10 MRI exams and then evaluated on the remaining 20 exams in each fold. Ourproposed method achieved a dice score of 0.904 and IoU of 0.822.

Automated Kidney Segmentation by Mask R-CNN in T2-weighted Magnetic Resonance Imaging