Exploration of Whole-Body Anatomy in the German National Cohort (NAKO): 3D Segmentation of 55 Structures in 28,969 MRI Scans

Motivation: Manual segmentation of large MRI datasets is time-consuming. Automated methods could speed up workflows and improve research in epidemiology and clinical settings. Goal(s): We validated the performance of the publicly available MRI deep learning-based TotalSegmentator by comparing it with quality-controlled annotations on 7,064 subjects. Approach: We segmented 55 structures in 28,969 subjects and extracted volume, diameter, and surface area. Bland-Altman plots assessed agreement with manually quality-controlled segmentations in 7,064 cases. Results: Automated segmentation showed high accuracy, though smaller structures like the pancreatic tail posed challenges. Bland-Altman analyses demonstrated strong agreement between automated and quality-controlled segmentations, highlighting the model's scalability and clinical research potential. Impact: This study validates deep learning-based segmentation of the TotalSegmentator model for large-scale MRI analysis (28,969 subjects), showing precise, scalable results. Automated and quality-controlled segmentations demonstrate strong agreement, highlighting its potential to advance research on anatomical structures and health outcomes.