A Novel Hybrid Model for Visceral Adipose Tissue Prediction using Shape Descriptors.
A Novel Hybrid Model for Visceral Adipose Tissue Prediction using Shape Descriptors.
复制标题
使用形状描述符预测内脏脂肪组织的新型混合模型。
DOI：
10.1109/embc.2019.8857092
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Hahn,JamesK
中科院分区：
文献类型：
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作者：
Wang,Qiyue;Lu,Yao;Zhang,Xiaoke;Hahn,JamesK
肥胖在现代社会越来越受到关注，因为它与各种健康问题有关。内脏脂肪组织（VAT）沉积在腹部器官周围，被认为是健康风险的一个极其重要的指标。增值税可以通过磁共振成像（MRI）或计算机断层扫描（CT）准确评估，但成本过高。由于商品光学身体扫描系统的普及，基于形状的身体成分预测已经成为一个有前途的课题，从中可以自动提取许多人体测量数据。在本文中，我们提出了一个创新的基于形状的混合增值税预测模型。我们的方法最吸引人的好处是有力地处理了性别和人口统计知识的缺乏。首先，我们分别为每个性别训练一个基线增值税预测模型。其次，我们训练了一个分类器来预测性别可能性和一个分类器来预测增值税基线预测中被高估的形状可能性。第三，我们将性别可能性和形状可能性整合到基线模型中，得出一个混合增值税预测模型。我们将我们的预测模型与其他最先进的增值税预测方法进行比较。结果表明，我们的方法平均比比较方法高出21.8%。
Obesity is gaining increasing attention in modern society since it is associated with various health issues. The visceral adipose tissue (VAT) deposits around the abdominal organs and is considered an extremely important indicator of health risk. VAT can be assessed through magnetic resonance imaging (MRI) or computed tomography (CT) accurately, but the cost is prohibitive. Shape-based body composition prediction has become a promising topic thanks to the prevalence of commodity optical body scan systems, from which numerous anthropometries can be extracted automatically. In this paper, we propose an innovative shape-based hybrid VAT prediction model. The most appealing benefit of our method is to robustly handle the lack of knowledge about gender and demographics. First, we train a baseline VAT prediction model for each gender separately. Second, we train a classifier to predict the gender likelihood and a classifier to predict the shape likelihood of being overestimated in VAT baseline prediction. Third, we integrate the gender likelihood and shape likelihood into the baseline models to derive one hybrid VAT prediction model. We compare our prediction model with other state-of-the-art VAT prediction methods. The result shows that our method outperforms the comparison methods by 21.8% on average.