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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)
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.
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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Visceral Fat; Obesity; Overweight; Mortality; Cohort
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
Shape in Medical Imaging
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 14350 LNCS,
Seiten: 105-117
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)
Förderungen
BMBF
NextGenerationEU of the European Union
ERC
NextGenerationEU of the European Union
ERC