This is a web GUI implementation of the algorithm described in the following paper:

    Tian IY, Wong MC, Kennedy SF, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, and Shepherd JA. A Device-Agnostic Shape Model for Automated Body Composition Estimates from 3D Optical Scans. Medical Physics. 2022. doi: 10.1002/mp.15843. PubMed PMID: 35837761.


    Background: Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and mre accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods.

    Purpose: We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices.

    Methods: A fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with BMI ranging from 14 to 52 kg/m2. Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained PCA domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per-vertex surface-to-surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model.

    Results: We achieved coefficients of determination (R2) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 was as high as 0.94 (total fat and lean, trunk fat) and all root-mean-squared errors (RMSE) were below 3.0 kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2-3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x.

    Conclusions: Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis.

    You must be logged in to use this tool. Please request an account or login.