Automated segmentation of kidneys and their sub-components is a challenging problem, particularly in pediatric patients and in the presence of a pathology or some anatomical deformation. We present a segmentation framework using a multimodal U-Net that allows for the automated segmentation of the multiple kidney components as well as a functional evaluation of the glomerular filtration rate. Results achieve an average Dice similarity coefficient of 0.912, 0.853, and 0.917 for kidney cortex, medulla, and collector system, respectively.
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