3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology

Authors

• Isaac Y. Tian, University of Washington Paul G. Allen School of Computer Science and Engineering, Seattle, WA
• Jason Liu, University of Washington Paul G. Allen School of Computer Science and Engineering, Seattle, WA
• Michael C. Wong, University of Hawaii Cancer Center, Honolulu, HI
• Nisa N. Kelly, University of Hawaii Cancer Center, Honolulu, HI
• Yong E. Liu, University of Hawaii Cancer Center, Honolulu, HI
• Andrea K. Gerber, University of California-San Francisco School of Medicine, San Francisco, CA
• Steven B. Heymsfield, Pennington Biomedical Research Center, Baton Rouge, LA
• Brian Curless, University of Washington Paul G. Allen School of Computer Science and Engineering, Seattle, WA
• John A. Shepherd, University of Hawaii Cancer Center, Honolulu, HI

Document Type

Article

Publication Date

2-2-2025

Publication Title

Digital Medicine

Abstract

Body composition prediction from 3D optical imagery has previously been studied with linear algorithms. In this study, we present a novel application of deep 3D convolutional graph networks and nonlinear Gaussian process regression for human body shape parameterization and body composition estimation. We trained and tested linear and nonlinear models with ablation studies on a novel ensemble body shape dataset containing 4286 scans. Nonlinear GPR produced up to a 20% reduction in prediction error and up to a 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only, and a 4-14% reduction in precision error for both sexes. All coefficients of determination (R2) for all predicted variables were above 0.86 and achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.

First Page

1

Last Page

13

DOI

10.1038/s41746-025-01469-6

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Tian, Isaac Y.; Liu, Jason; Wong, Michael C.; Kelly, Nisa N.; Liu, Yong E.; Gerber, Andrea K.; Heymsfield, Steven B.; Curless, Brian; and Shepherd, John A., "3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology" (2025). LSU-LCMC Cancer Center Faculty Publications. 187.
https://digitalscholar.lsuhsc.edu/llcc_facpubs/187
10.1038/s41746-025-01469-6