Abstract #3553

Attention-Based Deep Kidney Segmentation Framework for GFR Prediction

Edgar Rios Piedra^1,2, Morteza Mardani^1,2, and Shreyas Vasanawala^1,2

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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