Body Fat Estimation from Surface Meshes using Graph Neural Networks
- URL: http://arxiv.org/abs/2308.02493v3
- Date: Tue, 31 Oct 2023 09:04:24 GMT
- Title: Body Fat Estimation from Surface Meshes using Graph Neural Networks
- Authors: Tamara T. Mueller, Siyu Zhou, Sophie Starck, Friederike Jungmann,
Alexander Ziller, Orhun Aksoy, Danylo Movchan, Rickmer Braren, Georgios
Kaissis, Daniel Rueckert
- Abstract summary: 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.
- Score: 48.85291874087541
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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