PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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.* ; Machann, J.

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

Sci. Adv. 9:eadd0433 (2023)
Postprint DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Altmetric
13.600
0.000
1
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Multi-atlas Segmentation; Body-fat Distribution; Obesity; Population; Design; Burden; Images; Risk; Men
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2375-2548
e-ISSN 2375-2548
Zeitschrift Science Advances
Quellenangaben Band: 9, Heft: 19, Seiten: , Artikelnummer: eadd0433 Supplement: ,
Verlag American Association for the Advancement of Science (AAAS)
Verlagsort Washington, DC [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Diabetes Research and Metabolic Diseases (IDM)
Institute of Epidemiology (EPI)
POF Topic(s) 90000 - German Center for Diabetes Research
30202 - Environmental Health
Forschungsfeld(er) Helmholtz Diabetes Center
Genetics and Epidemiology
PSP-Element(e) G-502400-001
G-504000-010
Förderungen Leibniz Association
Helmholtz Association
federal states
Federal Ministry of Education and Research (BMBF)
German Federal Ministry of Education and Research (BMBF)
German Research Foundation
DOI 10.1126/sciadv.add0433
WOS ID 001004504000009
Scopus ID 85159738640
PubMed ID 37172093
Erfassungsdatum 2023-10-06
[➜Korrektur melden]