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.


DOI

doi:10.3238/arztebl.m2024.0040