PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Mueller, T.T.* ; Zhou, S.* ; Starck, S.* ; Jungmann, F.* ; Ziller, A.* ; Aksoy, O.* ; Movchan, D.* ; Braren, R.* ; Kaissis, G. ; Rueckert, D.*

Body Fat Estimation from Surface Meshes Using Graph Neural Networks.

In: (Shape in Medical Imaging). Berlin [u.a.]: Springer, 2023. 105-117 (Lect. Notes Comput. Sc. ; 14350 LNCS)
DOI
Body fat volume and distribution can be a strong indication for a person’s overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estimation are the body mass index (BMI), waist circumference, or the waist-hip-ratio. However, those are rather imprecise measures that do not allow for a discrimination between different types of fat or between fat and muscle tissue. The estimation of visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volume has shown to be a more accurate measure for named risk factors. In this work, we show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area. We furthermore envision this method to be applicable to cheaper and easily accessible medical surface scans instead of expensive medical images.
Altmetric
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: Konferenzbeitrag
Schlagwörter Visceral Fat; Obesity; Overweight; Mortality; Cohort
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Shape in Medical Imaging
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 14350 LNCS, Heft: , Seiten: 105-117 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Förderungen BMBF
NextGenerationEU of the European Union
ERC
DOI 10.1007/978-3-031-46914-5_9
WOS ID 001116075400009
Scopus ID 85177447422
Erfassungsdatum 2023-11-28
[➜Korrektur melden]