• 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.
• Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany; Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Germany; Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine Greifswald, Germany; Institute for Community Medicine, University Medicine Greifswald, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Germany; Centre for Advanced Analytics and Predictive Sciences (CAAPS), University of Augsburg, Germany; Department of Diagnostical and Interventional Radiology, University Hospital Heidelberg, Germany; Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Institute of Clinical Epidemiology and Biometry, University of Würzburg, State Institute of Health I, Bavarian Health and Food Safety Authority, Erlangen, Germany; Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group; Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; Chair of Genetic Epidemiology, University of Regensburg, Germany; Institute of Epidemiology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg; Chair of Epidemiology, Institute for Medical Information Processing, Biometrics, and Epidemiology, Medical Faculty, Ludwig-Maximilians-University Munich; DZHK (German Centre for Cardiovascular Research), Partner Site Munich, Munich Heart Alliance, Munich; DZD (German Centre for Diabetes Research), Neuherberg; Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin; Institute of Epidemiology, Kiel University, Kiel, Germany; Department of Diagnostic and Interventional Radiology, Core Facility MRDAC, University Medical Center Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Germany.
• Dtsch Arztebl Int. 2024 May 3; 121 (9): 284290284-290.

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 multiscale 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.

25 keywords...

hide…