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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: |
Haueise, T.  ORCID: https://orcid.org/0000-0002-1462-7539, Schick, F. ORCID: https://orcid.org/0000-0002-4231-3406, Stefan, N. ORCID: https://orcid.org/0000-0002-2186-9595, Schlett, C.L. ORCID: https://orcid.org/0000-0002-1576-1481, Weiss, J.B., Nattenmüller, J. ORCID: https://orcid.org/0000-0003-4032-378X, Göbel-Guéniot, K., Norajitra, T., Nonnenmacher, T., Kauczor, H.U., Maier-Hein, K.H. ORCID: https://orcid.org/0000-0002-6626-2463, Niendorf, T. ORCID: https://orcid.org/0000-0001-7584-6527, Pischon, T. ORCID: https://orcid.org/0000-0003-1568-767X, Jöckel, K.H. ORCID: https://orcid.org/0000-0002-1987-0255, Umutlu, L. ORCID: https://orcid.org/0000-0001-5215-7171, Peters, A. ORCID: https://orcid.org/0000-0001-6645-0985, Rospleszcz, S. ORCID: https://orcid.org/0000-0002-4788-2341, Kröncke, T. ORCID: https://orcid.org/0000-0003-4889-1036, Hosten, N. ORCID: https://orcid.org/0000-0002-4149-5666, Völzke, H., Krist, L. ORCID: https://orcid.org/0000-0002-6089-5163, Willich, S.N., Bamberg, F. and Machann, J. ORCID: https://orcid.org/0000-0002-4458-5886
|
| 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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