Elias Kellner, Peggy Sekula, Jan Lipovsek, Maximilian Russe, Harald Horbach, Christopher L Schlett, Matthias Nauck, Henry Völzke, Thomas Kroencke, Stefanie Bette, Hans-Ulrich Kauczor, Thomas Keil, Tobias Pischon, Iris M Heid, Annette Peters, Thoralf Niendorf, Wolfgang Lieb, Fabian Bamberg, Martin Büchert, Wilfried Reichardt, Marco Reisert, Anna Köttgen
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...
May 3, 2024: Deutsches Ärzteblatt International