Imaging markers derived from MRI-based automated kidney segmentation

Kellner, Elias; Sekula, Peggy; Lipovsek, Jan; Russe, Maximilian; Horbach, Harald; Schlett, Christopher L. ; Nauck, Matthias ; Völzke, Henry ; Kröncke, Thomas ; Bette, Stefanie; Kauczor, Hans-Ulrich; Keil, Thomas ; Pischon, Tobias ; Heid, Iris M.; Peters, Annette ; Niendorf, Thoralf ; Lieb, Wolfgang ; Bamberg, Fabian ; Büchert, Martin; Reichardt, Wilfried; Reisert, Marco und Köttgen, Anna (2024): Imaging markers derived from MRI-based automated kidney segmentation. In: Deutsches Ärzteblatt international, Bd. 121, Nr. 9: S. 284-290

Abstract

Background: Population-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).

Methods: We 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.

Results: There 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.

Conclusion: The 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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1866-0452
• Sprache: Englisch