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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


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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