MO061: Mri-Based Segmentation of Kidneys in Participants of the German National Cohort (NAKO/GNC) Study

Abstract

Abstract BACKGROUND AND AIMS Chronic kidney disease (CKD) affects around 10% of adults worldwide and an estimated 13–17% in Germany1. Imaging is a novel, promising approach to identify additional markers of kidney function and CKD2. Within a large, population-based cohort study, the German National Cohort study (NAKO/GNC), 30 000 participants underwent a whole-body MRI protocol3. The goal of our project was to develop an automated kidney segmentation workflow and to examine distributions of the segmented kidneys and kidney sub-compartments. METHOD Data from the first 11 207 participants were used to develop a robust image processing pipeline for kidney segmentation and apply it to participants’ abdominal MRI images. After importing 3D gradient echo and 2D haste images into the imaging platform NORA (www.nora-imaging.org), an in-house ‘patchwork’-framework (https://bitbucket.org/reisert/patchwork) based on deep-learning convolutional neural networks was trained on data from 300 persons to automatically segment different kidney compartments (cortex, hilus, medulla and cysts). After an initial training round, the model was improved over four iterations by a loop of prediction, manual correction and retraining. The final model was then applied to the full dataset of 11 207 abdominal MR images, followed by manual quality control prior to statistical analysis. Volumetric parameters for total kidney volume [TKV, defined as cortex + medulla] and compartments were calculated from the segmentations. Values were calculated in absolute units of mL and normalized to body-surface-area (BSA) defined as sqrt(weight in kg*height in cm)/3600. RESULTS TKV and the kidney compartments cortex, medulla and hilus could be segmented robustly with the trained network (Fig. 1A). After exclusion of approximately 10% of images because of insufficient segmentation quality due to initial imaging artifacts or poor image quality, the mean (SD) TKV was 364 (±60) mL for men and 290 (±51) mL for women. This difference was markedly attenuated after normalisation to BSA (Fig. 1B). The right kidney was systematically smaller than the left kidney by approximately 5% (Fig. 1C). There was a strong association between participants’ BSA with TKV (Fig. 1D). The normalized kidney sub-compartment volumes showed different patterns across age, with medullary volume decreasing and hilus increasing (Fig. 2). CONCLUSION The developed framework enables robust segmentation of kidneys in abdominal MRI data from a nonspecific clinical routine protocol of a large cohort study. Basic parameters such as TKV and sub-compartment volumes of the kidney show correlations with participants’ height, weight, sex and age that are consistent with prior knowledge and may enable an estimation of ‘kidney age’. This is an optimal starting point to identify more advanced imaging biomarkers of kidney function and CKD.