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：体脂体积和分布是反映个体整体健康状况及患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体重指数（BMI）、腰围或腰臀比，但这些指标精度较低，无法区分不同类型的脂肪或脂肪与肌肉组织。内脏脂肪组织（VAT）和腹部皮下脂肪组织（ASAT）体积的估算已被证实是评估上述风险因素更准确的指标。本研究表明，利用三角化体表网格结合图神经网络可精准预测VAT与ASAT体积。与当前该领域最先进的卷积神经网络相比，我们的方法在降低训练时间和资源需求的同时实现了高性能。我们进一步设想该方法可应用于成本更低、更易获取的医学表面扫描数据，而非昂贵的医学影像。