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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. and Schick, F. and Stefan, N. and Schlett, C.L. and Weiss, J.B. and Nattenmüller, J. and Göbel-Guéniot, K. and Norajitra, T. and Nonnenmacher, T. and Kauczor, H.U. and Maier-Hein, K.H. and Niendorf, T. and Pischon, T. and Jöckel, K.H. and Umutlu, L. and Peters, A. and Rospleszcz, S. and Kröncke, T. and Hosten, N. and Völzke, H. and Krist, L. and Willich, S.N. and 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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