Imaging markers derived from MRI-based automated kidney segmentation—an analysis of data from the German National Cohort (NAKO Gesundheitsstudie)
Autor/innen
- E. Kellner
- P. Sekula
- J. Lipovsek
- M. Russe
- H. Horbach
- C.L. Schlett
- M. Nauck
- H. Völzke
- T. Kröncke
- S. Bette
- H.U. Kauczor
- T. Keil
- T. Pischon
- I.M. Heid
- A. Peters
- T. Niendorf
- W. Lieb
- F. Bamberg
- M. Büchert
- W. Reichardt
- M. Reisert
- A. Köttgen
Journal
- Deutsches Arzteblatt International
Quellenangabe
- Dtsch Arztebl Int 121 (9): 284-290
Zusammenfassung
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 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. 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/m(2). 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 m(2) body surface area) was associated with a 0.98 mL/m(2) 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.