Abstract #1330

Workflow development for kidney segmentation using a U-net model using 11000 MRI data sets from the German National Cohort (NAKO/GNC) study

Martin Buechert¹, Jan Lipovsek², Marco Reisert², Harald Horbach², Wilfried Reichardt², Christopher Schlett², Fabian Bamberg², Peggy Sekula ², Anna Köttgen², and Elias Kellner²

¹Magnetic Resonance Development and Applicatione Center, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany, ²Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany

Synopsis

A processing pipeline for kidney segmentation using a hierarchical patch-based stack of U-nets was implemented and applied to abdominal MRI images of the German National Cohort study. The training data set included 300 cases and the final net was applied to the dataset of 11,207 MRIs. The compartments cortex, medulla and hilus could be segmented very robustly with the network. The relation of first parameters based on the segmentation withsex, age, weight, subject size and BMI are presented. This is an optimal starting point to identify more advanced biomarkers and their correlations, especially with kidney functional parameters.

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