{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:43:28Z","timestamp":1791650608377,"version":"4.3.4"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["25K15998"],"award-info":[{"award-number":["25K15998"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-02099-4","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:25:26Z","timestamp":1783610726000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-Based Segmental Volumetry of the Downstream Aorta in Aortic Dissection: End-to-End Versus Hybrid Strategies"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6455-5151","authenticated-orcid":false,"given":"Yu","family":"Nakano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoshi","family":"Nishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ikki","family":"Kojima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuke","family":"Shimahara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"2099_CR1","doi-asserted-by":"crossref","unstructured":"Dohle DS, Tsagakis K, Janosi RA, Benedik J, K\u00fchl H, Penkova L, Stebner F, Wendt D, Jakob H: Aortic remodelling in aortic dissection after frozen elephant trunk. Eur J Cardiothorac Surg 49:111-117, 2016","DOI":"10.1093\/ejcts\/ezv045"},{"key":"2099_CR2","doi-asserted-by":"crossref","unstructured":"Dohle DS, Jakob H, Schucht R, Janosi RA, Schlosser T, El Gabry M, Wendt D, Thielmann M, Tsagakis K: The impact of entries and exits on false lumen thrombosis and aortic remodelling. Eur J Cardiothorac Surg 52:508-515, 2017","DOI":"10.1093\/ejcts\/ezx236"},{"key":"2099_CR3","doi-asserted-by":"crossref","unstructured":"Usai MV, Ibrahim A, Oberhuber A, Dell\u2019Aquila AM, Martens S, Motekallemi A, Rukosujew A: Quantification of volume changes in the descending aorta after frozen elephant trunk procedure using the Thoraflex hybrid prosthesis for type A aortic dissection. J Thorac Dis 13:60-66, 2021","DOI":"10.21037\/jtd-20-2356"},{"key":"2099_CR4","doi-asserted-by":"crossref","unstructured":"Ibrahim A, Motekallemi A, Yahia A, Oberhuber A, Eierhoff T, Martens S, Marchiori E, Rukosujew A: Volume changes in the descending aorta after frozen elephant trunk and conventional hemi-arch repair after acute type A aortic dissection. Diagnostics (Basel) 12:2524, 2022","DOI":"10.3390\/diagnostics12102524"},{"key":"2099_CR5","doi-asserted-by":"crossref","unstructured":"Ferraresi M, Molinari ACL, Katsarou M, Rossi G: Volumetric analysis in primary and residual type B aortic dissection treated with stented-assisted balloon-induced intimal disruption and relamination (STABILISE) technique can predict aortic reintervention. J Vasc Surg 79:1315-1325, 2024","DOI":"10.1016\/j.jvs.2024.02.017"},{"key":"2099_CR6","doi-asserted-by":"crossref","unstructured":"Li Z, Feng J, Feng Z, An Y, Gao Y, Lu B et al.: Lumen segmentation of aortic dissection with cascaded convolutional network. In: Pop M, Sermesant M, Zhao J, Li S, McLeod K, Young A et al. (eds) Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Springer, Cham, pp 122\u2013130, 2019","DOI":"10.1007\/978-3-030-12029-0_14"},{"key":"2099_CR7","doi-asserted-by":"crossref","unstructured":"Hahn LD, Mistelbauer G, Higashigaito K, Koci M, Willemink MJ, Sailer AM, Fischbein M, Fleischmann D: CT-based true- and false-lumen segmentation in type B aortic dissection using machine learning. Radiol Cardiothorac Imaging 2:e190179, 2020","DOI":"10.1148\/ryct.2020190179"},{"key":"2099_CR8","doi-asserted-by":"crossref","unstructured":"Yao Z, Xie W, Zhang J, Dong Y, Qiu H, Yuan H, Jia Q, Wang T, Shi Y, Zhuang J, Que L, Xu X, Huang M: ImageTBAD: a 3D computed tomography angiography image dataset for automatic segmentation of type-B aortic dissection. Front Physiol 12:732711, 2021","DOI":"10.3389\/fphys.2021.732711"},{"key":"2099_CR9","doi-asserted-by":"crossref","unstructured":"Chen D, Zhang X, Mei Y, Liao F, Xu H, Li Z, Xiao Q, Guo W, Zhang H, Yan T, Xiong J, Ventikos Y: Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification. Med Image Anal 69:101931, 2021","DOI":"10.1016\/j.media.2020.101931"},{"key":"2099_CR10","doi-asserted-by":"crossref","unstructured":"Yu Y, Gao Y, Wei J, Liao F, Xiao Q, Zhang J, Yin W, Lu B: A three-dimensional deep convolutional neural network for automatic segmentation and diameter measurement of type B aortic dissection. Korean J Radiol 22:168-178, 2021","DOI":"10.3348\/kjr.2020.0313"},{"key":"2099_CR11","doi-asserted-by":"crossref","unstructured":"Huang LT, Tsai YS, Liou CF, Lee TH, Kuo PP, Huang HS, Wang CK: Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography. Eur Radiol 32:2277-2285, 2022","DOI":"10.1007\/s00330-021-08370-2"},{"key":"2099_CR12","doi-asserted-by":"crossref","unstructured":"Zhang X, Cheng G, Han X, Li S, Xiong J, Wu Z, Zhang H, Chen D: Deep learning-based multi-stage postoperative type-B aortic dissection segmentation using global-local fusion learning. Phys Med Biol 68:23, 2023","DOI":"10.1088\/1361-6560\/acfec7"},{"key":"2099_CR13","doi-asserted-by":"crossref","unstructured":"Kes\u00e4vuori R, Kaseva T, Salli E, Raivio P, Savolainen S, Kangasniemi M: Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection. Eur Radiol Exp 7:35, 2023","DOI":"10.1186\/s41747-023-00342-z"},{"key":"2099_CR14","doi-asserted-by":"crossref","unstructured":"Xiang D, Qi J, Wen Y, Zhao H, Zhang X, Qin J, Ma X, Ren Y, Hu H, Liu W, Yang F, Zhao H, Wang X, Zheng C: ADSeg: a flap-attention-based deep learning approach for aortic dissection segmentation. Patterns (N Y) 4:100727, 2023","DOI":"10.1016\/j.patter.2023.100727"},{"key":"2099_CR15","doi-asserted-by":"crossref","unstructured":"Zhuang C, Wu Y, Qi Q, Zhao S, Sun Y, Hou J, Qian W, Yang B, Qi S: A fully automatic pipeline of identification, segmentation, and subtyping of aortic dissection from CT angiography. Cardiovasc Eng Technol 16:465-480, 2025","DOI":"10.1007\/s13239-025-00787-w"},{"key":"2099_CR16","doi-asserted-by":"crossref","unstructured":"Song S, Qiu H, Huang M, Zhuang J, Lu Q, Shi Y, Li X, Xie W, Tong G, Xu X: Domain knowledge based comprehensive segmentation of type-A aortic dissection with clinically-oriented evaluation. Med Image Anal 102:103512, 2025","DOI":"10.1016\/j.media.2025.103512"},{"key":"2099_CR17","doi-asserted-by":"crossref","unstructured":"Fournel J, De Masi M, Lu C, Omnes V, Muselier B, Ghattas B, Bouchot O, Kafi M, Lalande A, Gaudry M, Jacquier A, Bartoli A: Deep learning-driven false-lumen volumes predict adverse remodeling better than diameter in patients with residual aortic dissection on CT. Eur Radiol 36, 3257-3270, 2026","DOI":"10.1007\/s00330-025-12116-9"},{"key":"2099_CR18","doi-asserted-by":"crossref","unstructured":"Krebs JR, Imran M, Fazzone B, Viscardi C, Berwick B, Stinson G, Heithaus E, Upchurch GR Jr, Shao W, Cooper MA: Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model. J Vasc Surg 80:1025-1034.e4, 2024","DOI":"10.1016\/j.jvs.2024.06.001"},{"key":"2099_CR19","doi-asserted-by":"crossref","unstructured":"Lombardi JV, Hughes GC, Appoo JJ, Bavaria JE, Beck AW, Cambria RP, Charlton-Ouw K, Eslami MH, Kim KM, Leshnower BG, Maldonado T, Reece TB, Wang GJ: Society for Vascular Surgery (SVS) and Society of Thoracic Surgeons (STS) reporting standards for type B aortic dissections. J Vasc Surg 71:723-747, 2020","DOI":"10.1016\/j.jvs.2019.11.013"},{"key":"2099_CR20","doi-asserted-by":"crossref","unstructured":"Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, Heye T, Boll DT, Cyriac J, Yang S, Bach M, Segeroth M: TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell 5:e230024, 2023","DOI":"10.1148\/ryai.230024"},{"key":"2099_CR21","doi-asserted-by":"crossref","unstructured":"Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203-211, 2021","DOI":"10.1038\/s41592-020-01008-z"},{"key":"2099_CR22","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D: Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi A, Bakas S (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham, pp 272\u2013284, 2022","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"2099_CR23","doi-asserted-by":"publisher","unstructured":"Ma J, Li F, Wang B: U-Mamba: enhancing long-range dependency for biomedical image segmentation. arXiv, https:\/\/doi.org\/10.48550\/arXiv.2401.04722, January 9, 2024","DOI":"10.48550\/arXiv.2401.04722"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02099-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02099-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02099-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:25:30Z","timestamp":1783610730000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02099-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"references-count":23,"alternative-id":["2099"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02099-4","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]},"assertion":[{"value":"24 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of Tsukuba Memorial Hospital (Date: [May 7th, 2025\/No. R7-04\u201302]).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Owing to the retrospective nature of the study, informed consent was obtained from all patients using the opt-out method.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}