{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T07:51:19Z","timestamp":1782201079595,"version":"3.54.5"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s00371-026-04550-7","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T18:52:39Z","timestamp":1781203959000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Slice-aware dual-channel Dixon MRI analysis for multi-region fat quantification: a two-stage visual computing framework"],"prefix":"10.1007","volume":"42","author":[{"given":"Yanan","family":"Duan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maocheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minmin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huating","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"issue":"1","key":"4550_CR1","first-page":"e102643","volume":"18","author":"N Akamatsu","year":"2026","unstructured":"Akamatsu, N., Gonoi, W., Hanaoka, S., et al.: Effects of contrast medium and vertebral measurement levels on computed tomography-based body composition parameters: skeletal muscle and adipose tissue analysis. Cureus 18(1), e102643 (2026)","journal-title":"Cureus"},{"key":"4550_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-30244-6","author":"GE Chung","year":"2026","unstructured":"Chung, G.E., Yoon, J.W., Kim, H., Han, Y.M., Choi, S.Y., Heo, N.J.: Association between visceral adipose tissue measured by deep neural network architecture and chronic kidney disease. Sci. Rep. (2026). https:\/\/doi.org\/10.1038\/s41598-025-30244-6","journal-title":"Sci. Rep."},{"issue":"5","key":"4550_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1136\/jim-2018-000722","volume":"66","author":"M Borga","year":"2018","unstructured":"Borga, M., West, J., Bell, J.D., et al.: Advanced body composition assessment: from body mass index to body composition profiling. J. Investig. Med. 66(5), 1\u20139 (2018)","journal-title":"J. Investig. Med."},{"key":"4550_CR4","doi-asserted-by":"crossref","unstructured":"Xi Y, Liang Z, Jin H, et al. Association of total and regional fat-to-muscle ratio with the risk of metabolic dysfunction-associated fatty liver disease and other chronic liver diseases. Hepatol Int (2026)","DOI":"10.1007\/s12072-026-11064-w"},{"key":"4550_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/cancers18030431","author":"S Rizzo","year":"2026","unstructured":"Rizzo, S., Petrella, F.: CT-assessed body composition as predictor of post-operative complications in lung cancer patients. Cancers (Basel) (2026). https:\/\/doi.org\/10.3390\/cancers18030431","journal-title":"Cancers (Basel)"},{"key":"4550_CR6","doi-asserted-by":"publisher","first-page":"103132","DOI":"10.1016\/j.clnesp.2026.103132","volume":"73","author":"M Caleffi","year":"2026","unstructured":"Caleffi, M., Padilha, D.M.H., Bassete, V., et al.: Determination of a new gastric cancer mortality predictor based on body composition radiodensity variables. Clin. Nutr. ESPEN 73, 103132 (2026)","journal-title":"Clin. Nutr. ESPEN"},{"key":"4550_CR7","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2025-014363","author":"K Grewal","year":"2026","unstructured":"Grewal, K., Moura Nascimento Santos, M.J., Chauhan, P.K., et al.: Influence of body composition on the efficacy of nivolumab plus ipilimumab for metastatic clear cell renal cell carcinoma. J. Immunother. Cancer (2026). https:\/\/doi.org\/10.1136\/jitc-2025-014363","journal-title":"J. Immunother. Cancer"},{"key":"4550_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-026-01535-4","author":"CP Kuss","year":"2026","unstructured":"Kuss, C.P., Ulbricht, L., Schumacher, K., Ripka, W.L.: Comparison between automated and manual segmentation in computed tomography for body composition analysis. Biomed. Eng. Online (2026). https:\/\/doi.org\/10.1186\/s12938-026-01535-4","journal-title":"Biomed. Eng. Online"},{"issue":"5","key":"4550_CR9","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1139\/H08-075","volume":"33","author":"M Mourtzakis","year":"2008","unstructured":"Mourtzakis, M., Prado, C.M., Lieffers, J.R., Reiman, T., McCargar, L.J., Baracos, V.E.: A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl. Physiol. Nutr. Metab. 33(5), 997\u20131006 (2008)","journal-title":"Appl. Physiol. Nutr. Metab."},{"key":"4550_CR10","doi-asserted-by":"crossref","unstructured":"Wolff DT, Evans JK, Rigdon J, et al. Body Composition Modulates Risk for Stress Urinary Incontinence in Women\u2009<\u200960. Int. Urogynecol. J. (2026)","DOI":"10.1007\/s00192-026-06589-5"},{"issue":"6","key":"4550_CR11","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1093\/ajcn\/48.6.1351","volume":"48","author":"H Kvist","year":"1988","unstructured":"Kvist, H., Chowdhury, B., Grang\u00e5rd, U., Tyl\u00e9n, U., Sj\u00f6str\u00f6m, L.: Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am. J. Clin. Nutr. 48(6), 1351\u20131361 (1988)","journal-title":"Am. J. Clin. Nutr."},{"issue":"3","key":"4550_CR12","doi-asserted-by":"publisher","first-page":"421","DOI":"10.62713\/aic.4229","volume":"97","author":"E Agosti","year":"2026","unstructured":"Agosti, E., Pagnoni, A., Zoia, C., et al.: Deep learning-based videomics for automatic segmentation in endoscopic endonasal surgery. Ann. Ital. Chir. 97(3), 421\u2013434 (2026)","journal-title":"Ann. Ital. Chir."},{"issue":"3","key":"4550_CR13","doi-asserted-by":"publisher","first-page":"e71969","DOI":"10.1002\/hsr2.71969","volume":"9","author":"MS Zarabadi","year":"2026","unstructured":"Zarabadi, M.S., Pirayesh, Z., Najary, S., Ghadimi, A.J., Behnaz, M.: Application of artificial intelligence in detecting dental anomalies: current models, imaging modalities, and future directions. Health Sci. Rep. 9(3), e71969 (2026)","journal-title":"Health Sci. Rep."},{"key":"4550_CR14","doi-asserted-by":"publisher","first-page":"1721194","DOI":"10.3389\/fradi.2026.1721194","volume":"6","author":"J Chen","year":"2026","unstructured":"Chen, J., Yuan, Q., Wang, H., et al.: Automated segmentation and classification of lumbar transverse ultrasound views using a two-stage deep learning method. Front. Radiol. 6, 1721194 (2026)","journal-title":"Front. Radiol."},{"key":"4550_CR15","doi-asserted-by":"publisher","first-page":"111993","DOI":"10.1016\/j.jocn.2026.111993","volume":"149","author":"G Urbanos","year":"2026","unstructured":"Urbanos, G., Casta\u00f1o-Le\u00f3n, A.M., Maldonado-Luna, M., et al.: Deep learning-based segmentation of aneurysmal subarachnoid hemorrhage: toward accurate and scalable prognostic imaging. J. Clin. Neurosci. 149, 111993 (2026)","journal-title":"J. Clin. Neurosci."},{"key":"4550_CR16","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. 2015; Cham: Springer International Publishing 234\u201341 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4550_CR17","unstructured":"Oktay O, Schlemper J, Le Folgoc L, et al. Attention U-Net: Learning where to look for the pancreas. ArXiv (2018) abs\/1804.03999."},{"issue":"2","key":"4550_CR18","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"4550_CR19","doi-asserted-by":"crossref","unstructured":"Wu MC, Tseng CZ, Huang YS, Chen CM. LRF-UNet: Low-rank factorized convolution deep-learning networks for visceral adipose and muscle tissue segmentation in abdominal computed tomography image. J. Imaging Inform. Med. (2026)","DOI":"10.1007\/s10278-025-01813-y"},{"issue":"2","key":"4550_CR20","doi-asserted-by":"publisher","first-page":"e70769","DOI":"10.1002\/cns.70769","volume":"32","author":"Y Su","year":"2026","unstructured":"Su, Y., Liu, T., Dong, C., et al.: An integrated QSM-Radiomics nomogram with clinical and imaging markers for stratifying cognitive impairment in hypertension. CNS Neurosci. Ther. 32(2), e70769 (2026)","journal-title":"CNS Neurosci. Ther."},{"key":"4550_CR21","doi-asserted-by":"publisher","first-page":"112747","DOI":"10.1016\/j.ejrad.2026.112747","volume":"198","author":"F Zeng","year":"2026","unstructured":"Zeng, F., Chen, C., Lin, H., et al.: Predicting axillary lymph node metastasis in clinical T1\/2 stage breast cancer using iodine map-derived multi-region radiomics and multi-modality imaging characteristics. Eur. J. Radiol. 198, 112747 (2026)","journal-title":"Eur. J. Radiol."},{"key":"4550_CR22","doi-asserted-by":"crossref","unstructured":"Zhang X, Vandekar S, Chen AA, et al. Multi-modal and multi-region distance model for neuroimaging: application to ABCD study. bioRxiv (2026)","DOI":"10.64898\/2026.01.21.700689"},{"key":"4550_CR23","doi-asserted-by":"publisher","first-page":"1723191","DOI":"10.3389\/fmed.2026.1723191","volume":"13","author":"N Alsharif","year":"2026","unstructured":"Alsharif, N., Nair, R., Aldhyani, T.H.H., Farhah, N.S., Ahmad, S., Al-Nefaie, A.H.: Vertebrae and intervertebral discs segmentation using deep learning-based model in disability analysis. Front. Med. 13, 1723191 (2026)","journal-title":"Front. Med."},{"key":"4550_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-026-02520-w","author":"G Hoyer","year":"2026","unstructured":"Hoyer, G., Tong, M.W., Bhattacharjee, R., Pedoia, V., Majumdar, S.: Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes. Npj Digit. Med. (2026). https:\/\/doi.org\/10.1038\/s41746-026-02520-w","journal-title":"Npj Digit. Med."},{"issue":"1","key":"4550_CR25","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1148\/radiology.153.1.6089263","volume":"153","author":"WT Dixon","year":"1984","unstructured":"Dixon, W.T.: Simple proton spectroscopic imaging. Radiology 153(1), 189\u2013194 (1984)","journal-title":"Radiology"},{"issue":"5","key":"4550_CR26","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1002\/jmri.1880010504","volume":"1","author":"GH Glover","year":"1991","unstructured":"Glover, G.H.: Multipoint Dixon technique for water and fat proton and susceptibility imaging. J. Magn. Reson. Imaging 1(5), 521\u2013530 (1991)","journal-title":"J. Magn. Reson. Imaging"},{"key":"4550_CR27","doi-asserted-by":"crossref","unstructured":"Rabinovich EP, Senthilvelan J, Foley CK, et al. Beyond BMI: Deep learning segmentation-driven CT reveals body composition changes after metabolic and bariatric surgery. J. Am. Coll. Surg. (2026)","DOI":"10.1097\/XCS.0000000000001902"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04550-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04550-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04550-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T07:42:35Z","timestamp":1782200555000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04550-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":27,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["4550"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04550-7","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"29 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"334"}}