{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:14:51Z","timestamp":1792782891074,"version":"4.5.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Russian Science Foundation","award":["25-75-30010"],"award-info":[{"award-number":["25-75-30010"]}],"id":[{"id":"https:\/\/ror.org\/03y2gwe85","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02350-y","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T16:22:28Z","timestamp":1791217348000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reference-Label Construction and Annotation Reporting in CT- and MRI-Based Adipose Tissue Segmentation: A Systematic Review and Evidence Map"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6231-8036","authenticated-orcid":false,"given":"Kopanitsa","family":"Georgy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yakovlev","family":"Alexey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babenko","family":"Alina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shlyakhto","family":"Evgeny","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"2350_CR1","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1016\/S0730-725X(03)00093-6","volume":"21","author":"LW Poll","year":"2003","unstructured":"L.W. Poll, H.J. Wittsack, J.A. Koch, R. Willers, M. Cohnen, C. Kapitza, L. Heinemann, U. M\u00f6dder, A rapid and reliable semiautomated method for measurement of total abdominal fat volumes using magnetic resonance imaging, Magn. Reson. Imaging 21 (2003) 631\u2013636. https:\/\/doi.org\/10.1016\/S0730-725X(03)00093-6.","journal-title":"Magn. Reson. Imaging"},{"key":"2350_CR2","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/SJ.IJO.0803696","volume":"32","author":"S Bonekamp","year":"2008","unstructured":"S. Bonekamp, P. Ghosh, S. Crawford, S.F. Solga, A. Horska, F.L. Brancati, A.M. Diehl, S. Smith, J.M. Clark, Quantitative comparison and evaluation of software packages for assessment of abdominal adipose tissue distribution by magnetic resonance imaging, Int. J. Obes. (Lond). 32 (2008) 100\u2013111. https:\/\/doi.org\/10.1038\/SJ.IJO.0803696.","journal-title":"Int. J. Obes. (Lond)"},{"key":"2350_CR3","doi-asserted-by":"publisher","unstructured":"T. Haueise, F. Schick, N. Stefan, C.L. Schlett, J.B. Weiss, J. Nattenm\u00fcller, K. G\u00f6bel-Gu\u00e9niot, T. Norajitra, T. Nonnenmacher, H.U. Kauczor, K.H. Maier-Hein, T. Niendorf, T. Pischon, K.H. J\u00f6ckel, L. Umutlu, A. Peters, S. Rospleszcz, T. Kr\u00f6ncke, N. Hosten, H. V\u00f6lzke, L. Krist, S.N. Willich, F. Bamberg, J. Machann, Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort, Sci. Adv. 9 (2023). https:\/\/doi.org\/10.1126\/SCIADV.ADD0433.","DOI":"10.1126\/SCIADV.ADD0433"},{"key":"2350_CR4","doi-asserted-by":"publisher","DOI":"10.1118\/1.4817577","volume":"40","author":"R Shahzad","year":"2013","unstructured":"R. Shahzad, D. Bos, C. Metz, A. Rossi, H. Kiri\u015fli, A. Van Der Lugt, S. Klein, J. Witteman, P. De Feyter, W. Niessen, L. Van Vliet, T. Van Walsum, Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach, Med. Phys. 40 (2013) 091910. https:\/\/doi.org\/10.1118\/1.4817577.","journal-title":"Med. Phys."},{"key":"2350_CR5","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1097\/RCT.0000000000000652","volume":"42","author":"P Decazes","year":"2018","unstructured":"P. Decazes, A. Rouquette, A. Chetrit, P. Vera, I. Gardin, Automatic Measurement of the Total Visceral Adipose Tissue From Computed Tomography Images by Using a Multi-Atlas Segmentation Method, J. Comput. Assist. Tomogr. 42 (2018) 139\u2013145. https:\/\/doi.org\/10.1097\/RCT.0000000000000652.","journal-title":"J. Comput. Assist. Tomogr."},{"key":"2350_CR6","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1109\/TMI.2018.2804799","volume":"37","author":"F Commandeur","year":"2018","unstructured":"F. Commandeur, M. Goeller, J. Betancur, S. Cadet, M. Doris, X. Chen, D.S. Berman, P.J. Slomka, B.K. Tamarappoo, D. Dey, Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT, IEEE Trans. Med. Imaging 37 (2018) 1835\u20131846. https:\/\/doi.org\/10.1109\/TMI.2018.2804799.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2350_CR7","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1093\/JAMIA\/OCAA342","volume":"28","author":"MT MacLean","year":"2021","unstructured":"M.T. MacLean, Q. Jehangir, M. Vujkovic, Y.A. Ko, H. Litt, A. Borthakur, H. Sagreiya, M. Rosen, D.A. Mankoff, M.D. Schnall, H. Shou, J. Chirinos, S.M. Damrauer, D.A. Torigian, R. Carr, D.J. Rader, W.R. Witschey, Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank, Journal of the American Medical Informatics Association 28 (2021) 1178\u20131187. https:\/\/doi.org\/10.1093\/JAMIA\/OCAA342.","journal-title":"Journal of the American Medical Informatics Association"},{"key":"2350_CR8","doi-asserted-by":"publisher","unstructured":"N. Ahmad, R. Strand, B. Sparres\u00e4ter, S. Tarai, E. Lundstr\u00f6m, G. Bergstr\u00f6m, H. Ahlstr\u00f6m, J. Kullberg, Automatic segmentation of large-scale CT image datasets for detailed body composition analysis, BMC Bioinformatics 24 (2023) 346-. https:\/\/doi.org\/10.1186\/S12859-023-05462-2\/TABLES\/4.","DOI":"10.1186\/S12859-023-05462-2\/TABLES\/4"},{"key":"2350_CR9","doi-asserted-by":"publisher","unstructured":"Z. Ma, C. Li, T. Du, L. Zhang, D. Tang, D. Ma, S. Huang, Y. Liu, Y. Sun, Z. Chen, J. Yuan, Q. Nie, M. Grzegorzek, H. Sun, AATCT-IDS: A benchmark Abdominal Adipose Tissue CT Image Dataset for image denoising, semantic segmentation, and radiomics evaluation, Comput. Biol. Med. 177 (2024). https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2024.108628.","DOI":"10.1016\/J.COMPBIOMED.2024.108628"},{"key":"2350_CR10","doi-asserted-by":"publisher","unstructured":"L.J. Kj\u00f8nigsen, M. Harneshaug, A.M. Fl\u00f8tten, L.K. Karterud, K. Petterson, G. Skjolde, H.B. Eggesb\u00f8, H. Weedon-Fekj\u00e6r, H.B. Henriksen, P.M. Lauritzen, Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study, Eur. Radiol. Exp. 3 (2019). https:\/\/doi.org\/10.1186\/S41747-019-0122-5.","DOI":"10.1186\/S41747-019-0122-5"},{"key":"2350_CR11","doi-asserted-by":"publisher","unstructured":"S.L. Gomez-Perez, Y. Zhang, C. Byrne, C. Wakefield, T. Geesey, J. Sclamberg, S. Peterson, Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction, Sensors (Basel) 22 (2022). https:\/\/doi.org\/10.3390\/S22093357.","DOI":"10.3390\/S22093357"},{"key":"2350_CR12","doi-asserted-by":"publisher","unstructured":"K.A. Wahid, B. Olson, R. Jain, A.J. Grossberg, D. El-Habashy, C. Dede, V. Salama, M. Abobakr, A.S.R. Mohamed, R. He, J. Jaskari, J. Sahlsten, K. Kaski, C.D. Fuller, M.A. Naser, Muscle and adipose tissue segmentations at the third cervical vertebral level in patients with head and neck cancer, Sci. Data 9 (2022). https:\/\/doi.org\/10.1038\/S41597-022-01587-W.","DOI":"10.1038\/S41597-022-01587-W"},{"key":"2350_CR13","doi-asserted-by":"publisher","unstructured":"K. Tam, S.W. Liu, S. Costa, E. Szabo, S. Reitsma, H. Gillick, J.D. Adachi, A.K.O. Wong, Fully-automated segmentation of muscle and inter-\/intra-muscular fat from magnetic resonance images of calves and thighs: an open-source workflow in Python, Skelet. Muscle 14 (2024). https:\/\/doi.org\/10.1186\/S13395-024-00365-Z.","DOI":"10.1186\/S13395-024-00365-Z"},{"key":"2350_CR14","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1016\/J.ACRA.2019.07.006","volume":"26","author":"L Lenchik","year":"2019","unstructured":"L. Lenchik, L. Heacock, A.A. Weaver, R.D. Boutin, T.S. Cook, J. Itri, C.G. Filippi, R.P. Gullapalli, J. Lee, M. Zagurovskaya, T. Retson, K. Godwin, J. Nicholson, P.A. Narayana, Automated Segmentation of Tissues Using CT and MRI: A Systematic Review, Acad. Radiol. 26 (2019) 1695\u20131706. https:\/\/doi.org\/10.1016\/J.ACRA.2019.07.006.","journal-title":"Acad. Radiol."},{"key":"2350_CR15","doi-asserted-by":"publisher","unstructured":"L. Zhang, J. Sun, B. Jiang, L. Wang, Y. Zhang, X. Xie, Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review, Eur. J. Hybrid Imaging 5 (2021) 14-. https:\/\/doi.org\/10.1186\/S41824-021-00107-0\/FIGURES\/5.","DOI":"10.1186\/S41824-021-00107-0\/FIGURES\/5"},{"key":"2350_CR16","doi-asserted-by":"publisher","unstructured":"S. Bedrikovetski, W. Seow, H.M. Kroon, L. Traeger, J.W. Moore, T. Sammour, Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis, Eur. J. Radiol. 149 (2022). https:\/\/doi.org\/10.1016\/J.EJRAD.2022.110218.","DOI":"10.1016\/J.EJRAD.2022.110218"},{"key":"2350_CR17","doi-asserted-by":"publisher","unstructured":"C. Winder, M. Clark, R. Frood, L. Smith, A. Bulpitt, G. Cook, A. Scarsbrook, Automated extraction of body composition metrics from abdominal CT or MR imaging: A scoping review, Eur. J. Radiol. 181 (2024). https:\/\/doi.org\/10.1016\/J.EJRAD.2024.111764.","DOI":"10.1016\/J.EJRAD.2024.111764"},{"key":"2350_CR18","doi-asserted-by":"publisher","unstructured":"M.J. Page, J.E. Mckenzie, P.M. Bossuyt, I. Boutron, T.C. Hoffmann, C.D. Mulrow, L. Shamseer, J.M. Tetzlaff, E.A. Akl, S.E. Brennan, R. Chou, J. Glanville, J.M. Grimshaw, A. Hr\u00f3bjartsson, M.M. Lalu, T. Li, E.W. Loder, E. Mayo-Wilson, S. Mcdonald, L.A. Mcguinness, L.A. Stewart, J. Thomas, A.C. Tricco, V.A. Welch, P. Whiting, D. Moher, The PRISMA 2020 statement: an updated guideline for reporting systematic reviews, (n.d.). https:\/\/doi.org\/10.1136\/bmj.n71.","DOI":"10.1136\/bmj.n71"},{"key":"2350_CR19","doi-asserted-by":"publisher","unstructured":"A.S. Tejani, M.E. Klontzas, A.A. Gatti, J.T. Mongan, L. Moy, S.H. Park, C.E. Kahn, Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update, Radiol. Artif. Intell. 6 (2024). https:\/\/doi.org\/10.1148\/RYAI.240300.","DOI":"10.1148\/RYAI.240300"},{"key":"2350_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1590\/0100-3984.2017.0211","volume":"52","author":"LC Pescatori","year":"2019","unstructured":"L.C. Pescatori, E. Savarino, G. Mauri, E. Silvestri, M. Cariati, F. Sardanelli, L.M. Sconfienza, Quantification of visceral adipose tissue by computed tomography and magnetic resonance imaging: reproducibility and accuracy, Radiol. Bras. 52 (2019) 1\u20136. https:\/\/doi.org\/10.1590\/0100-3984.2017.0211.","journal-title":"Radiol. Bras."},{"key":"2350_CR21","doi-asserted-by":"publisher","first-page":"8957","DOI":"10.1007\/S00330-023-09865-W\/TABLES\/4","volume":"33","author":"D Schneider","year":"2023","unstructured":"D. Schneider, T. Eggebrecht, A. Linder, N. Linder, A. Schaudinn, M. Bl\u00fcher, T. Denecke, H. Busse, Abdominal fat quantification using convolutional networks, Eur. Radiol. 33 (2023) 8957\u20138964. https:\/\/doi.org\/10.1007\/S00330-023-09865-W\/TABLES\/4.","journal-title":"Eur. Radiol."},{"key":"2350_CR22","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/J.JSS.2018.11.027","volume":"237","author":"T Srikumar","year":"2019","unstructured":"T. Srikumar, E.M. Siegel, Y. Gu, Y. Balagurunathan, A.L. Garcia, Y.A. Chen, J.M. Zhou, X. Zhao, R. Gillies, W. Clark, A. Gamenthaler, J. Choi, D. Shibata, Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis, J. Surg. Res. 237 (2019) 12\u201321. https:\/\/doi.org\/10.1016\/J.JSS.2018.11.027.","journal-title":"J. Surg. Res."},{"key":"2350_CR23","doi-asserted-by":"publisher","unstructured":"S.S. Kim, J.H. Kim, W.K. Jeong, J. Lee, Y.K. Kim, D. Choi, W.J. Lee, Semiautomatic software for measurement of abdominal muscle and adipose areas using computed tomography: A STROBE-compliant article, Medicine 98 (2019). https:\/\/doi.org\/10.1097\/MD.0000000000015867.","DOI":"10.1097\/MD.0000000000015867"},{"key":"2350_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1148\/RYAI.2020200010","volume":"2","author":"T K\u00fcstner","year":"2020","unstructured":"T. K\u00fcstner, T. Hepp, M. Fischer, M. Schwartz, A. Fritsche, H.U. H\u00e4ring, K. Nikolaou, F. Bamberg, B. Yang, F. Schick, S. Gatidis, J. Machann, Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies, Radiol. Artif. Intell. 2 (2020) 1\u201313. https:\/\/doi.org\/10.1148\/RYAI.2020200010.","journal-title":"Artif. Intell."},{"key":"2350_CR25","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3348\/KJR.2019.0470","volume":"21","author":"HJ Park","year":"2020","unstructured":"H.J. Park, Y. Shin, J. Park, H. Kim, I.S. Lee, D.W. Seo, J. Huh, T.Y. Lee, T. Park, J. Lee, K.W. Kim, Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography, Korean J. Radiol. 21 (2020) 88\u2013100. https:\/\/doi.org\/10.3348\/KJR.2019.0470.","journal-title":"Korean J. Radiol."},{"key":"2350_CR26","doi-asserted-by":"publisher","unstructured":"S. Masoudi, S.M. Anwar, S.A. Harmon, P.L. Choyke, B. Turkbey, U. Bagci, Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation, Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2020 (2020) 1624\u20131628. https:\/\/doi.org\/10.1109\/EMBC44109.2020.9176009.","DOI":"10.1109\/EMBC44109.2020.9176009"},{"key":"2350_CR27","doi-asserted-by":"publisher","unstructured":"Q. Yang, X. Yu, H.H. Lee, Y. Tang, S. Bao, K.S. Gravenstein, A.Z. Moore, S. Makrogiannis, L. Ferrucci, B.A. Landman, Label efficient segmentation of single slice thigh CT with two-stage pseudo labels, J. Med. Imaging (Bellingham) 9 (2022). https:\/\/doi.org\/10.1117\/1.JMI.9.5.052405.","DOI":"10.1117\/1.JMI.9.5.052405"},{"key":"2350_CR28","doi-asserted-by":"publisher","unstructured":"F.O. Hofmann, C. Heiliger, T. Tschaidse, S. Jarmusch, L.A. Auhage, U. Aghamaliyev, A.B. Gesenhues, T.S. Schiergens, H. Niess, M. Ilmer, J. Werner, B.W. Renz, Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies, Scientific Reports 2025 15:1 15 (2025) 11909-. https:\/\/doi.org\/10.1038\/s41598-025-96238-6.","DOI":"10.1038\/s41598-025-96238-6"},{"key":"2350_CR29","doi-asserted-by":"publisher","unstructured":"F. Prinzi, C. Militello, G. Sollami, P. Toia, L. La Grutta, S. Vitabile, MultiD4CAD: Multimodal Dataset composed of CT and Clinical Features for Coronary Artery Disease Analysis, Sci. Data 12 (2025). https:\/\/doi.org\/10.1038\/S41597-025-05743-W.","DOI":"10.1038\/S41597-025-05743-W"},{"key":"2350_CR30","doi-asserted-by":"publisher","unstructured":"M. Mandzak, E. Yang, A. Zapaishchykova, Y.-H. Chen, L. Heilbroner, J. Zielke, D. Tak, R. Mojahed-Yazdi, F.R. Mussa, Z. Ye, S. Vajapeyam, V. Benitez, R. Salloum, S.N. Chi, H. Sotoudeh, J. Seidlitz, S. Mueller, H.J.W.L. Aerts, T.Y. Poussaint, B.H. Kann, TissUnet: Improved extracranial tissue and cranium segmentation for children through adulthood, Imaging Neuroscience (Cambridge, Mass.) 4 (2026). https:\/\/doi.org\/10.1162\/IMAG.A.1067.","DOI":"10.1162\/IMAG.A.1067"},{"key":"2350_CR31","doi-asserted-by":"publisher","unstructured":"R.F. Cabini, A. Cozzi, S. Leu, B. Thelen, R. Krause, F. Del Grande, D.U. Pizzagalli, S.M.R. Rizzo, CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans, Eur. Radiol. Exp. 9 (2025). https:\/\/doi.org\/10.1186\/S41747-025-00552-7.","DOI":"10.1186\/S41747-025-00552-7"},{"key":"2350_CR32","doi-asserted-by":"publisher","DOI":"10.1117\/12.844810","volume":"7623","author":"D Dey","year":"2010","unstructured":"D. Dey, A. Ramesh, P.J. Slomka, R. Nakazato, V.Y. Cheng, G. Germano, D.S. Berman, Automated algorithm for atlas-based segmentation of the heart and pericardium from non-contrast CT, Proceedings of SPIE 7623 (2010) 762337. https:\/\/doi.org\/10.1117\/12.844810.","journal-title":"Proceedings of SPIE"},{"key":"2350_CR33","doi-asserted-by":"publisher","first-page":"97840U","DOI":"10.1117\/12.2216841","volume":"9784","author":"Z Xu","year":"2016","unstructured":"Z. Xu, R.B. Baucom, R.G. Abramson, B.K. Poulose, B.A. Landman, Whole Abdominal Wall Segmentation using Augmented Active Shape Models (AASM) with Multi-Atlas Label Fusion and Level Set, Proc. SPIE. Int. Soc. Opt. Eng. 9784 (2016) 97840U. https:\/\/doi.org\/10.1117\/12.2216841.","journal-title":"Proc. SPIE. Int. Soc. Opt. Eng."},{"key":"2350_CR34","doi-asserted-by":"publisher","unstructured":"K.X. Tang, X.B. Liao, L.Q. Yuan, S.Q. He, M. Wang, X.L. Mei, Z.A. Zhou, Q. Fu, X. Lin, J. Liu, An enhanced deep learning method for the quantification of epicardial adipose tissue, Sci. Rep. 14 (2024). https:\/\/doi.org\/10.1038\/S41598-024-75659-9.","DOI":"10.1038\/S41598-024-75659-9"},{"key":"2350_CR35","doi-asserted-by":"publisher","unstructured":"J. Ha, T. Park, H.K. Kim, Y. Shin, Y. Ko, D.W. Kim, Y.S. Sung, J. Lee, S.J. Ham, S. Khang, H. Jeong, K. Koo, J. Lee, K.W. Kim, Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography, Scientific Reports 2021 11:1 11 (2021) 21656-. https:\/\/doi.org\/10.1038\/s41598-021-00161-5.","DOI":"10.1038\/s41598-021-00161-5"},{"key":"2350_CR36","doi-asserted-by":"publisher","unstructured":"R. Graf, P. Platzek, E.O. Riedel, C. Ramsch\u00fctz, S. Starck, H.K. M\u00f6ller, M. Atad, H. V\u00f6lzke, R. B\u00fclow, C.O. Schmidt, J. R\u00fcdebusch, M. Jung, M. Reisert, J. Weiss, M.T. L\u00f6ffler, F. Bamberg, B. Wiestler, J.C. Paetzold, D. Rueckert, J.S. Kirschke, VIBESegmentator: full body MRI segmentation for the NAKO and UK Biobank, Eur. Radiol. 36 (2026). https:\/\/doi.org\/10.1007\/S00330-025-12035-9.","DOI":"10.1007\/S00330-025-12035-9"},{"key":"2350_CR37","doi-asserted-by":"publisher","unstructured":"S. Leng, N. Cheng, E. Tan, L. Baskaran, L. Teo, M. Sen Yew, K.Y. Ngiam, W. Huang, P. Chai, C.C. Ong, C.H. Sia, M. Singh, Y.T. Loong, N.A.S. Raffiee, X. Wang, J. Allen, S.Y. Tan, M. Chan, H.K. Lee, L. Zhong, Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study, European Heart Journal. Digital Health 6 (2025) 1223\u20131233. https:\/\/doi.org\/10.1093\/EHJDH\/ZTAF116.","DOI":"10.1093\/EHJDH\/ZTAF116"},{"key":"2350_CR38","doi-asserted-by":"publisher","unstructured":"R. Amer, J. Nassar, A. Trabelsi, D. Bendahan, H. Greenspan, N. Ben-Eliezer, Quantification of Intra-Muscular Adipose Infiltration in Calf\/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation, Bioengineering (Basel) 9 (2022). https:\/\/doi.org\/10.3390\/BIOENGINEERING9070315.","DOI":"10.3390\/BIOENGINEERING9070315"},{"key":"2350_CR39","doi-asserted-by":"publisher","unstructured":"G. Gonz\u00e1lez, G.R. Washko, R. San Jos\u00e9 Est\u00e9par, Multi-structure Segmentation from Partially Labeled Datasets. Application to Body Composition Measurements on CT Scans, Image Analysis for Moving Organ, Breast, and Thoracic Images\u202f: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada,... 11040 (2018) 215\u2013224. https:\/\/doi.org\/10.1007\/978-3-030-00946-5_22.","DOI":"10.1007\/978-3-030-00946-5_22"},{"key":"2350_CR40","doi-asserted-by":"publisher","unstructured":"V. Fauveau, H. Patel, J. Prevot, B. Xu, O. Cohen, S. Khan, P.M. Robson, Z.A. Fayad, C. Lippert, H. Greenspan, N. Shah, V. Kundel, A Pre-Trained Model Customization Framework for Accelerated PET\/MR Segmentation of Abdominal Fat in Obstructive Sleep Apnea, Diagnostics 15 (2025). https:\/\/doi.org\/10.3390\/DIAGNOSTICS15243243.","DOI":"10.3390\/DIAGNOSTICS15243243"},{"key":"2350_CR41","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1007\/S11548-025-03380-7\/FIGURES\/6","volume":"20","author":"T Boucher","year":"2025","unstructured":"T. Boucher, N. Tetlow, A. Fung, A. Dewar, P. Arina, S. Kerneis, J. Whittle, E.B. Mazomenos, KEVS: enhancing segmentation of visceral adipose tissue in pre-cystectomy CT with Gaussian kernel density estimation, International Journal of Computer Assisted Radiology and Surgery 20 (2025) 1335\u20131344. https:\/\/doi.org\/10.1007\/S11548-025-03380-7\/FIGURES\/6.","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"2350_CR42","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1007\/S11548-024-03167-2","volume":"19","author":"B Hou","year":"2024","unstructured":"B. Hou, T.S. Mathai, J. Liu, C. Parnell, R.M. Summers, Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study, Int. J. Comput. Assist. Radiol. Surg. 19 (2024) 1589\u20131596. https:\/\/doi.org\/10.1007\/S11548-024-03167-2.","journal-title":"Int. J. Comput. Assist. Radiol. Surg."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02350-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02350-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02350-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:03:03Z","timestamp":1791262983000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02350-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,5]]},"references-count":42,"alternative-id":["2350"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02350-y","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,5]]},"assertion":[{"value":"7 August 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable. This systematic review synthesizes information from published studies and did not involve recruitment of human participants, collection of new human or animal data, or access to identifiable individual-level data.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}