{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T01:00:51Z","timestamp":1769562051922,"version":"3.49.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032142450","type":"print"},{"value":"9783032142467","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-14246-7_7","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T12:48:17Z","timestamp":1769518097000},"page":"74-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AortaST: A Student-Teacher Framework for\u00a0Multi-class Aortic Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3102-1595","authenticated-orcid":false,"given":"Abdul","family":"Qayyum","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4444-5776","authenticated-orcid":false,"given":"Moona","family":"Mazher","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4612-6982","authenticated-orcid":false,"given":"Steven A.","family":"Niederer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"7_CR1","first-page":"107547","volume":"37","author":"M Beck","year":"2024","unstructured":"Beck, M., et al.: xLSTM: extended long short-term memory. Adv. Neural. Inf. Process. Syst. 37, 107547\u2013107603 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2024.102470","volume":"118","author":"M Imran","year":"2024","unstructured":"Imran, M., et al.: CIS-UNet: multi-class segmentation of the aorta in computed tomography angiography via context-aware shifted window self-attention. Comput. Med. Imaging Graph. 118, 102470 (2024)","journal-title":"Comput. Med. Imaging Graph."},{"key":"7_CR3","unstructured":"Imran, M., et\u00a0al.: Multi-class segmentation of aortic branches and zones in computed tomography angiography: the aortaseg24 challenge. arXiv preprint arXiv:2502.05330 (2025)"},{"issue":"4","key":"7_CR4","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1016\/j.jvs.2024.06.001","volume":"80","author":"JR Krebs","year":"2024","unstructured":"Krebs, J.R., et al.: Volumetric analysis of acute uncomplicated type B aortic dissection using an automated deep learning aortic zone segmentation model. J. Vasc. Surg. 80(4), 1025\u20131034 (2024)","journal-title":"J. Vasc. Surg."},{"key":"7_CR5","unstructured":"Li, X., et\u00a0al.: The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: the instance challenge. arXiv preprint arXiv:2301.03281 (2023)"},{"key":"7_CR6","unstructured":"Luo, G., et al.: Tumor detection. Presented at the (2025)"},{"key":"7_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102256","volume":"106","author":"M Mazher","year":"2024","unstructured":"Mazher, M., et al.: Self-supervised spatial-temporal transformer fusion based federated framework for 4D cardiovascular image segmentation. Inf. Fus. 106, 102256 (2024)","journal-title":"Inf. Fus."},{"key":"7_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103253","volume":"97","author":"Y Nan","year":"2024","unstructured":"Nan, Y., et al.: Hunting imaging biomarkers in pulmonary fibrosis: benchmarks of the AIIB23 challenge. Med. Image Anal. 97, 103253 (2024)","journal-title":"Med. Image Anal."},{"key":"7_CR9","unstructured":"Oquab, M., et\u00a0al.: DINOv2: learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"7_CR10","unstructured":"Payette, K., et\u00a0al.: Multi-center fetal brain tissue annotation (feta) challenge 2022 results. IEEE Trans. Med. Imaging (2024)"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Qayyum, A., Ang, C.K., Sridevi, S., Khan, M.A., Hong, L.W., Mazher, M., Chung, T.D.: Hybrid 3D-ResNet deep learning model for automatic segmentation of thoracic organs at risk in CT images. In: 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), Sochi, Russia, 2020, pp. 1\u20135. IEEE (2020)","DOI":"10.1109\/ICIEAM48468.2020.9111950"},{"key":"7_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102226","volume":"106","author":"A Qayyum","year":"2024","unstructured":"Qayyum, A., Razzak, I., Mazher, M., Lu, X., Niederer, S.A.: Unsupervised unpaired multiple fusion adaptation aided with self-attention generative adversarial network for scar tissues segmentation framework. Inf. Fus. 106, 102226 (2024)","journal-title":"Inf. Fus."},{"key":"7_CR13","unstructured":"Qayyum, A., et al.: Transforming heart chamber imaging: self-supervised learning for whole heart reconstruction and segmentation. arXiv preprint arXiv:2406.06643 (2024)"},{"key":"7_CR14","unstructured":"de\u00a0la Rosa, E., et\u00a0al.: A robust ensemble algorithm for ischemic stroke lesion segmentation: generalizability and clinical utility beyond the isles challenge. arXiv preprint arXiv:2403.19425 (2024)"},{"key":"7_CR15","unstructured":"de\u00a0la Rosa, E., et\u00a0al.: ISLES\u201924: improving final infarct prediction in ischemic stroke using multimodal imaging and clinical data. arXiv preprint arXiv:2408.10966 (2024)"},{"key":"7_CR16","unstructured":"Yang, K., et\u00a0al.: Benchmarking the CoW with the TopCoW challenge: topology-aware anatomical segmentation of the circle of Willis for CTA and MRA. ArXiv, arXiv\u20132312 (2025)"},{"key":"7_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102957","volume":"90","author":"M Zhang","year":"2023","unstructured":"Zhang, M., et al.: Multi-site, multi-domain airway tree modeling. Med. Image Anal. 90, 102957 (2023)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Multi-class Segmentation of the Aorta"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-14246-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T12:48:18Z","timestamp":1769518098000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-14246-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032142450","9783032142467"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-14246-7_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"28 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"All data used was publicly available and anonymized.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Compliance Statement"}},{"value":"AortaSeg","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Challenge on Multi-class Segmentation of the Aorta","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aortaseg2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aortaseg24.grand-challenge.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}