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Body Fat Estimation from Surface Meshes Using Graph Neural Networks

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Shape in Medical Imaging (ShapeMI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14350))

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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.

T. T. Mueller and S. Zhou—These authors contributed equally to this work.

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Acknowledgements

TM and SS were supported by the ERC (Deep4MI - 884622). This work has been conducted under the UK Biobank application 87802. SS has furthermore been supported by BMBF and the NextGenerationEU of the European Union.

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Authors and Affiliations

  1. Institute for AI in Medicine and Healthcare, Faculty of Informatics, Technical University of Munich, Munich, Germany

    Tamara T. Mueller, Siyu Zhou, Sophie Starck, Alexander Ziller, Orhun Aksoy, Danylo Movchan, Georgios Kaissis & Daniel Rueckert

  2. Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Munich, Germany

    Tamara T. Mueller, Friederike Jungmann, Alexander Ziller, Rickmer Braren & Georgios Kaissis

  3. Department of Computing, Imperial College London, London, UK

    Daniel Rueckert

  4. Institute for Machine Learning in Biomedical Imaging, Helmholtz-Zentrum Munich, Munich, Germany

    Georgios Kaissis

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  1. Tamara T. Mueller
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Correspondence to Tamara T. Mueller .

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Editors and Affiliations

  1. Technical University of Munich, Munich, Germany

    Christian Wachinger

  2. University of North Carolina at Chapel Hill, Carrboro, NC, USA

    Beatriz Paniagua

  3. University of Utah, Salt Lake City, UT, USA

    Shireen Elhabian

  4. Essen University Hospital, Essen, Germany

    Jianning Li

  5. Essen University Hospital, Essen, Germany

    Jan Egger

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Mueller, T.T. et al. (2023). Body Fat Estimation from Surface Meshes Using Graph Neural Networks. In: Wachinger, C., Paniagua, B., Elhabian, S., Li, J., Egger, J. (eds) Shape in Medical Imaging. ShapeMI 2023. Lecture Notes in Computer Science, vol 14350. Springer, Cham. https://doi.org/10.1007/978-3-031-46914-5_9

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  • DOI: https://doi.org/10.1007/978-3-031-46914-5_9

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