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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