A pose‐independent method for accurate and precise body composition from 3D optical scans

Abstract

Objective The aim of this study was to investigate whether digitally re‐posing three‐dimensional optical (3DO) whole‐body scans to a standardized pose would improve body composition accuracy and precision regardless of the initial pose. Methods Healthy adults (n = 540), stratified by sex, BMI, and age, completed whole‐body 3DO and dual‐energy X‐ray absorptiometry (DXA) scans in the Shape Up! Adults study. The 3DO mesh vertices were represented with standardized templates and a low‐dimensional space by principal component analysis (stratified by sex). The total sample was split into a training (80%) and test (20%) set for both males and females. Stepwise linear regression was used to build prediction models for body composition and anthropometry outputs using 3DO principal components (PCs). Results The analysis included 472 participants after exclusions. After re‐posing, three PCs described 95% of the shape variance in the male and female training sets. 3DO body composition accuracy compared with DXA was as follows: fat mass R2 = 0.91 male, 0.94 female; fat‐free mass R2 = 0.95 male, 0.92 female; visceral fat mass R2 = 0.77 male, 0.79 female. Conclusions Re‐posed 3DO body shape PCs produced more accurate and precise body composition models that may be used in clinical or nonclinical settings when DXA is unavailable or when frequent ionizing radiation exposure is unwanted.

Accuracy and Precision of 3D Optical Imaging for Body Composition and their Associations to Metabolic Markers by Age, BMI, and Ethnicity

“…As a whole, the accuracy and precision were similar to previous work done by Wong et al (26). Methods to develop the statistical shape model and the body composition equations were the exact same.…”

“…Due to the power needed in each subgroup, splitting the total sample in this analysis into a representative training and test set by subgroup was not possible due to the low representations in some subgroups (i.e., underweight and NHOPI). However, the previous work showed how these methods were robust and validated strongly in its accuracy (26).…”

“…This means each vertex corresponds to a specific anatomical location across all registered meshes. All meshes were digitally reposed to a T-pose, where the person was standing straight, arms were brought horizontal and in plane with the body, and arms and legs were straightened (26, 31).…”

“…They showed that body shape descriptors by the 3DO mesh were more predictive of body composition as compared to DXA and were correlated to metabolic biomarkers (23). Since then, the methodology to obtain these body shape descriptors have improved by incorporating automated processing methods, agnostic models across multiple 3DO scanners, pose-independent models, and a 2D to 3D pipeline (24)(25)(26)(27). Although much has been accomplished in this field in a short amount of time, 3DO body composition has not been evaluated at the subgroup level.…”

3D Optical Body Composition Accuracy across Subgroups of BMI and Race/Ethnicity

“…Each participant received whole-body 3DO and DXA scans on a Fit3D Proscanner (Fit3D Inc., San Mateo, CA, USA) and Hologic Horizon/A or Discovery/A system (Hologic Inc., Marlborough, MA, USA), respectively. 3DO scans were templated with a 110,000-vertex mesh for standardization and reposed through Meshcapade (Meshcapade GmbH, Tubingen, Germany) [6]. Principal component (PC) analysis was performed on the 3DO scans to reduce the dimensionality of the data to explain 95% of the shape variance with three PCs.…”