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This work aimed to develop and evaluate automated abdominal SAT\/VAT segmentation on longitudinal MRI in adults with overweight\/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>920 adults with overweight\/obesity were scanned twice at multiple 3\u00a0T MRI scanners and institutions. The first scan was divided into training\/validation\/testing sets (n\u2009=\u2009646\/92\/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>ACD 3D U-Net achieved rapid (&lt;\u20094.8\u00a0s\/subject) segmentation with high DICE-SAT (median\u2009\u2265\u20090.994) and DICE-VAT (median\u2009\u2265\u20090.976), small FN (median\u2009\u2264\u20090.7%), and FP (median\u2009\u2264\u20091.1%). 3D nnU-Net yielded rapid (&lt;\u20092.5\u00a0s\/subject) segmentation with similar DICE-SAT (median\u2009\u2265\u20090.992), DICE-VAT (median\u2009\u2265\u20090.979), FN (median\u2009\u2264\u20091.1%) and FP (median\u2009\u2264\u20091.2%). Both models yielded excellent agreement in SAT\/VAT volume versus reference measurements (ICC\u2009&gt;\u20090.997) in longitudinal analysis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Discussion<\/jats:title>\n                <jats:p>ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT\/VAT volume rapidly, accurately, and longitudinally in adults with overweight\/obesity.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s10334-023-01146-3","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T11:02:08Z","timestamp":1706785328000},"page":"491-506","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5941-5399","authenticated-orcid":false,"given":"Sevgi Gokce","family":"Kafali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu-Fu","family":"Shih","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhou","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grace Hyun J.","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tristan","family":"Kelly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilpy","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Spencer","family":"Loong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy","family":"Moretz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel R.","family":"Barnes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2585-5916","authenticated-orcid":false,"given":"Holden H.","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"issue":"10","key":"1146_CR1","doi-asserted-by":"publisher","first-page":"4461","DOI":"10.21037\/qims-21-370","volume":"11","author":"F Greco","year":"2021","unstructured":"Greco F, Mallio CA (2021) Artificial intelligence and abdominal adipose tissue analysis: a literature review. 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The International Application Number is PCT\/US2022\/048221 and the International Publication Number is WO 2023\/076596 A1. The inventors are Holden H. Wu, Ph.D. and Sevgi Gokce Kafali, M.S. Grace Hyun J. Kim, Ph.D. is a research consultant at MedQIA. 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