• Elias Kellner, Peggy Sekula, Jan Lipovsek, Maximilian Russe, Harald Horbach, Christopher L Schlett, Matthias Nauck, Henry Völzke, Thomas Kroencke, Stefanie Bette, Hans-Ulrich Kauczor, Thomas Keil, Tobias Pischon, Iris M Heid, Annette Peters, Thoralf Niendorf, Wolfgang Lieb, Fabian Bamberg, Martin Büchert, Wilfried Reichardt, Marco Reisert, and Anna Köttgen.
• Dtsch Arztebl Int. 2024 May 3 (Forthcoming).

BackgroundPopulation-wide research on potential new imaging biomarkers of the kidney depends on accurate automated segmentation of the kidney and its compartments (cortex, medulla, and sinus).MethodsWe developed a robust deep-learning framework for kidney (sub-)segmentation based on a hierarchical, three-dimensional convolutional neural network (CNN) that was optimized for multi-scale problems of combined localization and segmentation. We applied the CNN to abdominal magnetic resonance images from the population-based German National Cohort (NAKO) study.ResultsThere was good to excellent agreement between the model predictions and manual segmentations. The median values for the body-surface normalized total kidney, cortex, medulla, and sinus volumes of 9934 persons were 158, 115, 43, and 24 mL/m2. Distributions of these markers are provided both for the overall study population and for a subgroup of persons without kidney disease or any associated conditions. Multivariable adjusted regression analyses revealed that diabetes, male sex, and a higher estimated glomerular filtration rate (eGFR) are important predictors of higher total and cortical volumes. Each increase of eGFR by one unit (i.e., 1 mL/min per 1.73 m2 body surface area) was associated with a 0.98 mL/m2 increase in total kidney volume, and this association was significant. Volumes were lower in persons with eGFR-defined chronic kidney disease.ConclusionThe extraction of image-based biomarkers through CNN-based renal sub-segmentation using data from a population-based study yields reliable results, forming a solid foundation for future investigations.

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