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.

翻译：体脂体积及其分布是评估个体整体健康状况及罹患2型糖尿病、心血管疾病等风险的重要指标。常用的脂肪估算指标包括身体质量指数（BMI）、腰围或腰臀比，但这些测量方式精度较低，无法区分不同脂肪类型或脂肪与肌肉组织。研究表明，内脏脂肪组织（VAT）与腹部皮下脂肪组织（ASAT）体积的估算是上述风险因素更准确的评估指标。本文证明，利用三角化人体表面网格结合图神经网络可精确预测VAT与ASAT体积。与传统卷积神经网络在该领域的应用相比，本方法在保持高性能的同时减少了训练时间与资源需求。此外，该方法有望应用于成本更低、更易获取的人体表面扫描数据，而非昂贵的医学影像。