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| Item Type: | Article |
|---|---|
| Title: | Imaging markers derived from MRI-based automated kidney segmentation—an analysis of data from the German National Cohort (NAKO Gesundheitsstudie) |
| Creators Name: | Kellner, E., Sekula, P., Lipovsek, J., Russe, M., Horbach, H., Schlett, C.L., Nauck, M., Völzke, H., Kröncke, T., Bette, S., Kauczor, H.U., Keil, T., Pischon, T., Heid, I.M., Peters, A., Niendorf, T., Lieb, W., Bamberg, F., Büchert, M., Reichardt, W., Reisert, M. and Köttgen, A. |
| 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 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. |
| Keywords: | Biomarkers, Cohort Studies, Computer Neural Networks, Deep Learning, Germany, Glomerular Filtration Rate, Kidney, Magnetic Resonance Imaging |
| Source: | Deutsches Arzteblatt International |
| ISSN: | 1866-0452 |
| Publisher: | Deutscher Aerzte-Verlag |
| Volume: | 121 |
| Number: | 9 |
| Page Range: | 284-290 |
| Date: | 3 May 2024 |
| Official Publication: | https://doi.org/10.3238/arztebl.m2024.0040 |
| PubMed: | View item in PubMed |
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