A Novel Hybrid Model for Visceral Adipose Tissue Prediction using Shape Descriptors

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.