Pooled Two-Cohort MRI Body Composition Phenotyping With Open-Source Deep Learning
MCML Authors
Daniel Rückert
Prof. Dr.
Director
Abstract
Daniel Rückert
Prof. Dr.
Director
Abstract
Background: Body mass index fails to capture variation in fat and muscle distribution that determines metabolic health and disease risk. MRI enables radiation-free quantification of regional body composition, yet scalable open-source tools applied in pooled cohorts with differing acquisition protocols have been lacking.<br>Methods: MRSegmentator, an open-source nnU-Net-based pipeline, was applied to quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), gluteofemoral adipose tissue (GFAT), trunk musculature, and the liver mask used for liver fat-fraction estimation in 45,851 adults from the German National Cohort (n = 26,877, 3 T multi-centre Siemens) and UK Biobank (n = 18,974, 1.5 T Siemens). Population-scale compartment volumes were segmented from stitched in-phase gradient-echo (GRE) images in both cohorts; liver fat fraction was calculated from fat-only and water-only images. The annotated development data comprised NAKO T2-HASTE and UKB Dixon reconstructions. A single pooled model was applied without site-specific adaptation. A separate two-reader agreement study used 50 scans from these annotated development-sequence domains. Associations between BMI-adjusted body composition and cardiometabolic conditions were estimated using generalized linear mixed-effects models. Incremental discrimination beyond age, BMI, and waist-to-hip ratio was assessed.<br>Results: Five-fold participant-stratified internal cross-validation against curated human-in-the-loop development references comprising UKB Dixon and NAKO T2-HASTE yielded a mean Dice of 0.91. In a separate 50-scan reader study on these annotated development-sequence images, overall reader–reader Dice was 0.937 and overall algorithm–reader Dice was 0.908. The trained pipeline was then used to segment compartment volumes from stitched in-phase GRE inputs in both cohorts, while liver fat fraction was calculated from fat-only and water-only images; direct sequence-matched validation on NAKO GRE was not performed. VAT showed the strongest positive associations with cardiometabolic conditions, while GFAT showed inverse associations, most prominently for type 2 diabetes (OR 0.69, 95% CI 0.66 to 0.72). Disease-specific body-composition phenotypes were identified, with type 2 diabetes characterized by elevated VAT, reduced GFAT, and increased liver fat. MRI-derived compartments modestly improved discrimination for type 2 diabetes and hyperlipidemia beyond anthropometric measures.<br>Conclusions: A single open-source deep-learning pipeline enabled pooled body-composition phenotyping in two cohorts and captured distributional variation in fat and muscle beyond BMI. High agreement in internal cross-validation (mean Dice 0.91) and the separate two-reader study support the annotated development-sequence analysis, while the population-scale application identified distinct disease-associated phenotypes and modest incremental discrimination beyond conventional anthropometry.
article MHZ+26
Communications Medicine
6.467. Aug. 2026.Authors
C. J. Mertens • H. Häntze • S. Ziegelmayer • J. N. Kather • D. Truhn • S. H. Kim • F. Busch • D. Weller • B. Wiestler • M. Graf • F. Bamberg • C. L. Schlett • J. B. Weiss • S. Ringhof • E. Can • J. Schulz-Menger • T. Niendorf • J. Lammert • I. Molwitz • A. Kader • A. Hering • A. Meddeb • J. Nawabi • M. B. Schulze • T. Keil • S. N. Willich • L. Krist • M. Hadamitzky • A. Hannemann • F. Bassermann • D. Rückert • T. Pischon • A. Hapfelmeier • M. R. Makowski • K. K. Bressem • L. C. AdamsLinks
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