{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:05:22Z","timestamp":1784693122951,"version":"3.55.0"},"reference-count":17,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>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 more accessible than criterion body composition assessment methods such as dual\u2010energy X\u2010ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>We introduce a scanner\u2010agnostic 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A fixed\u2010topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52\u00a0kg\/m<jats:sup>2<\/jats:sup>. Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (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\u2010vertex surface\u2010to\u2010surface 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We achieved coefficients of determination (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> was as high as 0.94 (total fat and lean, trunk fat), and all root\u2010mean\u2010squared errors were below 3.0\u00a0kg. 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\u20133x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation\u2010free alternative to criterion radiology imaging for body composition analysis. We published a web\u2010app version of this work at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/shapeup.shepherdresearchlab.org\/3do-bodycomp-analyzer\/\">https:\/\/shapeup.shepherdresearchlab.org\/3do\u2010bodycomp\u2010analyzer\/<\/jats:ext-link> that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15843","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T07:35:15Z","timestamp":1657870515000},"page":"6395-6409","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A device\u2010agnostic shape model for automated body composition estimates from 3D optical scans"],"prefix":"10.1002","volume":"49","author":[{"given":"Isaac Y.","family":"Tian","sequence":"first","affiliation":[{"name":"Paul G. Allen School of Computer Science and Engineering University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael C.","family":"Wong","sequence":"additional","affiliation":[{"name":"University of Hawaii Cancer Center University of Hawaii \u2010 Manoa  Honolulu Hawaii USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samantha","family":"Kennedy","sequence":"additional","affiliation":[{"name":"Pennington Biomedical Research Center Louisiana State University  Baton Rouge Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nisa N.","family":"Kelly","sequence":"additional","affiliation":[{"name":"University of Hawaii Cancer Center University of Hawaii \u2010 Manoa  Honolulu Hawaii USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong E.","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Hawaii Cancer Center University of Hawaii \u2010 Manoa  Honolulu Hawaii USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea K.","family":"Garber","sequence":"additional","affiliation":[{"name":"UCSF School of Medicine University of California \u2010 San Francisco  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven B.","family":"Heymsfield","sequence":"additional","affiliation":[{"name":"Pennington Biomedical Research Center Louisiana State University  Baton Rouge Louisiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Curless","sequence":"additional","affiliation":[{"name":"Paul G. Allen School of Computer Science and Engineering University of Washington  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John A.","family":"Shepherd","sequence":"additional","affiliation":[{"name":"University of Hawaii Cancer Center University of Hawaii \u2010 Manoa  Honolulu Hawaii USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10654\u2010010\u20109459\u2010z"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.165.7.777"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0068716"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1161\/circulationaha.107.739714"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/ejcn.2016.109"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/oby.22637"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/ajcn\/nqz218"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1201775.882311"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14492"},{"key":"e_1_2_11_11_1","doi-asserted-by":"crossref","unstructured":"RobinetteKM BlackwellS DaanenH BoehmerM FlemingS.Civilian American and European Surface Anthropometry Resource (CAESAR) Final Report. 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