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Abstract #0706

Automatic Renal Cortex Segmentation Using Machine Learning for MR Urography

Umit Yoruk1,2, Brian Hargreaves2, and Shreyas Vasanawala2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Glomerular filtration rate (GFR) estimation can be achieved using dynamic contrast enhanced MRI (DCE-MRI) and pharmacokinetic models. The segmentation of kidneys is essential for obtaining the time intensity curves needed by these models. Manual segmentation of kidneys is one of the most time consuming and labor-intensive steps of GFR analysis as it can take several hours and require trained personnel. Here, we introduce a novel method for automatic renal segmentation based on morphological segmentation and machine learning, and assess the performance of the method.

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