Manual segmentation of the kidneys in renal MRI is a time consuming process in many processing pipelines. Existing automated methods using classical imaging processing are specific to a single pathology. Here we implement a convolutional neural network for rapid and automatic segmentation of the kidneys from both a healthy control and Chronic Kidney Disease cohort. When validated on unseen data, the network achieved a mean Dice score of 0.93±0.02 with mean error in total kidney volume of 2.0±16.5 ml which, in the majority of subjects, was better than human precision from manual segmentation.
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