3D body shape for regional and appendicular body composition estimation

Yijiang Zheng; Zhuoxin Long; Xiaoke Zhang; James K. Hahn

doi:10.1117/12.2653964

3 April 2023 3D body shape for regional and appendicular body composition estimation

Yijiang Zheng, Zhuoxin Long, Xiaoke Zhang, James K. Hahn

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124642C (2023) https://doi.org/10.1117/12.2653964
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Body composition is correlated to bone mineral density, muscle strength, and physical performance. This is important for diagnosing conditions like sarcopenia, which is defined as the age-associated decrease in muscle mass leading to decreased mobile function, increased frailty, and imbalance. Existing methods for body composition measurement either suffer from inaccurate results or require expensive equipment such as Dual-energy x-ray absorptiometry (DXA). Although DXA measures lean mass and not muscle mass, previous studies have considered extremity lean mass as appendicular skeletal muscle mass (ASMM) approximation. In this study, we develop a new shape descriptor to predict regional body composition (in particular, regional lean mass) from 3D body shapes. In addition, we propose a neural network for ASMM assessment which is calculated by lean mass. We evaluate the effectiveness by comparing adjusted R-Squared values and Root Mean Square Error (RMSE). In our experiment, the regression models utilizing level circumference as the training feature outperforms all regional anthropometric measurements and lowers the average RMSE by about 21%. For ASMM, the proposed neural network, which combines shape features and demographic features, surpasses all other traditional regression models and reaches the lowest RMSE at 1.85 kg. Compared to the vanilla linear regression model, our approach improves the RMSE by 17%. The experimental results suggest that the 3D body shape has the potential to be used to predict body composition, and in particular lean mass, for the whole body as well as specific regions of the body.

Citation Download Citation

Yijiang Zheng, Zhuoxin Long, Xiaoke Zhang, and James K. Hahn "3D body shape for regional and appendicular body composition estimation", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124642C (3 April 2023); https://doi.org/10.1117/12.2653964

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