ArticleScience advances2023
Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort.
Article in Science advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Adaptive deep learning for quantification and spatial distribution of abdominal adipose tissue from magnetic resonance imaging proton density fat fraction in adults with a body mass index ≥24 kg/mQuantitative imaging in medicine and surgery · 2026Article
- Refining visceral adipose tissue quantification: Influence of sex, age, and BMI on single slice estimation in 3D MRI of the German National Cohort.Zeitschrift fur medizinische Physik · 2026Article
- Cross-cohort federated learning for pediatric abdominal adipose tissue segmentation and quantification using free-breathing 3D MRI.Radiology advances · 2026Article
- Association of a Lifestyle Risk Index With Visceral and Subcutaneous Adipose Tissue in the German National Cohort (NAKO).Obesity (Silver Spring, Md.) · 2026Article
- Evaluation of Projection Images for Visual Quality Control of Automated Left and Right Lung Segmentations on T1-Weighted MRI in Large-Scale Clinical Cohort Studies.Tomography (Ann Arbor, Mich.) · 2025Article
- Association Between Body Composition and Cardiometabolic Outcomes : A Prospective Cohort Study.Annals of internal medicine · 2025Observational
- MR-based fat fraction changes in subcutaneous adipose tissue in people with obesity undergoing a weight loss intervention: results from the LION study.Quantitative imaging in medicine and surgery · 2025Article
- Automated Field of View Prescription for Whole-body Magnetic Resonance Imaging Using Deep Learning Based Body Region Segmentations.Investigative radiology · 2025Article
- Reproducibility of automatic adipose tissue segmentation using proton density fat fraction images between 1.5 and 3.0 T magnetic resonance.Quantitative imaging in medicine and surgery · 2025Article
- Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.Magma (New York, N.Y.) · 2024Article
- MRI Methods to Visualize and Quantify Adipose Tissue in Health and Disease.Biomedicines · 2023Review
Corrections and comments
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Authors and funding
24 authors at 11 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)-based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of >0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index-related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.