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Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort

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Item Type:Article
Title:Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort
Creators Name:Haueise, T., Schick, F., Stefan, N., Schlett, C.L., Weiss, J.B., Nattenmüller, J., Göbel-Guéniot, K., Norajitra, T., Nonnenmacher, T., Kauczor, H.U., Maier-Hein, K.H., Niendorf, T., Pischon, T., Jöckel, K.H., Umutlu, L., Peters, A., Rospleszcz, S., Kröncke, T., Hosten, N., Völzke, H., Krist, L., Willich, S.N., Bamberg, F. and Machann, J.
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.
Keywords:Adipose Tissue, Cohort Studies, Insulin Resistance, Magnetic Resonance Imaging, Risk Factors
Source:Science Advances
ISSN:2375-2548
Publisher:American Association for the Advancement of Science
Volume:9
Number:19
Page Range:eadd0433
Date:12 May 2023
Official Publication:https://doi.org/10.1126/sciadv.add0433
PubMed:View item in PubMed

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