Predicting 3D body shape and body composition from conventional 2D photography. Issue 12 (20th October 2020)

• Record Type: Journal Article
• Authors: Tian, Isaac Y.; Ng, Bennett K.; Wong, Michael C.; Kennedy, Samantha; Hwaung, Phoenix; Kelly, Nisa; Liu, En; Garber, Andrea K.; Curless, Brian; Heymsfield, Steven B.; Shepherd, John A.
• Is Part Of: Medical physics. Volume 47:Issue 12(2020)
• Page Start: 6232
• Page End: 6245
• Publication Date: 2020-10-20
• Subjects: body composition -- dual‐energy x‐ray absorptiometry -- nutritional assessment -- principal components analysis -- silhouette -- obesity; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14492
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Total and regional body composition are important indicators of health and mortality risk, but their measurement is usually restricted to controlled environments in clinical settings with expensive and specialized equipment. A method that approaches the accuracy of the current gold standard method, dual‐energy x‐ray absorptiometry (DXA), while only requiring input from widely available consumer grade equipment, would enable the measurement of these important biometrics in the wild, enabling data collection at a scale that would have previously been prohibitive in time and expense. We describe an algorithm for predicting three‐dimensional (3D) body shape and composition from a single frontal 2‐dimensional image acquired with a digital consumer camera. Methods: Duplicate 3D optical scans, two‐dimensional (2D) optical images, and DXA whole‐body scans were available for 183 men and 233 women from the Shape Up! Adults Study. A principal component analysis vector basis was fit to 3D point clouds of a training subset of 152 men and 194 women. The relationship between this vector space and DXA‐derived body composition was modeled with linear regression. The principal component 3D shape was then fitted to match a silhouette extracted from a 2D photograph of a novel body. Body composition was predicted from the resulting 3D shape match using the linear mapping between the principal component parameters and the DXA metrics. Accuracy of body composition estimates from the silhouette method was evaluated against a simple model using height and weight as a baseline, and against DXA measurements as ground truth. Test‐retest precision of the silhouette method was evaluated using the duplicate 2D optical images and compared against precision of the duplicate DXA scans. Paired t ‐tests were performed to detect significant differences between the sets. Results: Results were reported on a held‐out set. Body composition prediction achieved R 2 s of 0.81 and 0.74 for percent fat prediction of males and females, respectively, on a held‐out test set consisting of 31 males and 39 females. Precision estimates for fat mass were 2.31% and 2.06% for males and females, respectively, compared to 1.26% and 0.68% for DXA scans. The t ‐tests revealed no statistically significant differences between the silhouette method measurements and DXA measurements, or between retests. Conclusion: Total and regional body composition measures can be estimated from a single frontal photograph of a human body. Body composition prediction using consumer level photography can enable early screening and monitoring of possible physiological indicators of metabolic disease in regions where medical imagery or clinical assessment is inaccessible. Abstract :