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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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Publication type
Article: Conference contribution
Keywords
Visceral Fat; Obesity; Overweight; Mortality; Cohort
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
Shape in Medical Imaging
Quellenangaben
Volume: 14350 LNCS,
Pages: 105-117
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)
Grants
BMBF
NextGenerationEU of the European Union
ERC
NextGenerationEU of the European Union
ERC