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Abstract #3553

Attention-Based Deep Kidney Segmentation Framework for GFR Prediction

Edgar Rios Piedra1,2, Morteza Mardani1,2, and Shreyas Vasanawala1,2
1Department of Radiology, Stanford University, Stanford, CA, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States

Automated segmentation of kidneys and their sub-components is a challenging problem, particularly in pediatric patients and in the presence of anatomical deformations or pathology. We present an improved segmentation framework using a multi-channel U-Net with added attention block that allows for the automated segmentation of the multi-phase DCE-MRI of kidneys 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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