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Abstract #0254

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

Louisa Fay1,2,3, Qi Wang1, Bin Yang2, Thomas Kuestner1, and Sergios Gatidis3
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 3Stanford Medicine, Department of Radiology, Stanford, CA, United States

Synopsis

Keywords: Analysis/Processing, Data Analysis

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.

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Keywords