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Abstract #0811

Automated Renal Segmentation in Healthy and Chronic Kidney Disease Subjects Using A Convolutional Neural Network

Alexander J Daniel1, Charlotte E Buchanan1, Thomas Allcock1, Daniel Scerri1, Eleanor F Cox1, Benjamin L Prestwich1, and Susan T Francis1
1Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom

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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