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Abstract #4380

DeepKidney: Deep segmentation of MR images for automated glomerular function quantification in heterogeneous pediatric patients

Edgar A. Rios Piedra1, Morteza Mardani1, Ukash Nakarmi1, Joseph Y. Cheng1, and Shreyas S. Vasanawala1

1Department of Radiology, Department of Electrical Engineering, Stanford University, Palo Alto, CA, United States

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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