{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T23:52:58Z","timestamp":1781481178047,"version":"3.54.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032162700","type":"print"},{"value":"9783032162717","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\/978-3-032-16271-7_7","type":"book-chapter","created":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T23:21:23Z","timestamp":1781479283000},"page":"68-78","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CardioSeqM: A Scalable and Context-Aware Model for Unified Heart Segmentation from Volumetric Cardiac Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3102-1595","authenticated-orcid":false,"given":"Abdul","family":"Qayyum","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4444-5776","authenticated-orcid":false,"given":"Moona","family":"Mazher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4612-6982","authenticated-orcid":false,"given":"Steven A","family":"Niederer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102889","volume":"89","author":"S Gao","year":"2023","unstructured":"Gao, S., Zhou, H., Gao, Y., Zhuang, X.: Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability. Med. Image Anal. 89, 102889 (2023)","journal-title":"Med. Image Anal."},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI brainlesion workshop. pp. 272\u2013284. Springer (2021)","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"7_CR3","first-page":"110876","volume":"37","author":"S Hwang","year":"2024","unstructured":"Hwang, S., Lahoti, A., Puduppully, R., Dao, T., Gu, A.: Hydra: Bidirectional state space models through generalized matrix mixers. Adv. Neural. Inf. Process. Syst. 37, 110876\u2013110908 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR4","unstructured":"Imran, M., Krebs, J.R., Sivaraman, V.B., Zhang, T., Kumar, A., Ueland, W.R., Fassler, M.J., Huang, J., Sun, X., Wang, L., et\u00a0al.: Multi-class segmentation of aortic branches and zones in computed tomography angiography: The aortaseg24 challenge. arXiv preprint arXiv:2502.05330 (2025)"},{"issue":"2","key":"7_CR5","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., 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":"7_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102256","volume":"106","author":"M Mazher","year":"2024","unstructured":"Mazher, M., Razzak, I., Qayyum, A., Tanveer, M., Beier, S., Khan, T., Niederer, S.A.: Self-supervised spatial-temporal transformer fusion based federated framework for 4d cardiovascular image segmentation. Inf. Fusion 106, 102256 (2024)","journal-title":"Inf. Fusion"},{"key":"7_CR7","unstructured":"Munk, A., Ambsdorf, J., Llambias, S., Nielsen, M.: Amaes: Augmented masked autoencoder pretraining on public brain mri data for 3d-native segmentation. arXiv preprint arXiv:2408.00640 (2024)"},{"key":"7_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103253","volume":"97","author":"Y Nan","year":"2024","unstructured":"Nan, Y., Xing, X., Wang, S., Tang, Z., Felder, F.N., Zhang, S., Ledda, R.E., Ding, X., Yu, R., Liu, W., 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","doi-asserted-by":"crossref","unstructured":"Payette, K., Steger, C., Licandro, R., De\u00a0Dumast, P., Li, H.B., Barkovich, M., Li, L., Dannecker, M., Chen, C., Ouyang, C., et\u00a0al.: Multi-center fetal brain tissue annotation (feta) challenge 2022 results. IEEE transactions on medical imaging (2024)","DOI":"10.1109\/TMI.2024.3485554"},{"key":"7_CR10","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). pp.\u00a01\u20135. IEEE (2020)","DOI":"10.1109\/ICIEAM48468.2020.9111950"},{"key":"7_CR11","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. Fusion 106, 102226 (2024)","journal-title":"Inf. Fusion"},{"key":"7_CR12","unstructured":"Qayyum, A., Xu, H., Halliday, B.P., Rodero, C., Lanyon, C.W., Wilkinson, R.D., Niederer, S.A.: Transforming heart chamber imaging: Self-supervised learning for whole heart reconstruction and segmentation. arXiv preprint arXiv:2406.06643 (2024)"},{"key":"7_CR13","unstructured":"de\u00a0la Rosa, E., Reyes, M., Liew, S.L., Hutton, A., Wiest, R., Kaesmacher, J., Hanning, U., Hakim, A., Zubal, R., Valenzuela, W., 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_CR14","unstructured":"de\u00a0la Rosa, E., Su, R., Reyes, M., Wiest, R., Riedel, E.O., Kofler, F., Yang, K., Baazaoui, H., Robben, D., Wegener, S., 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_CR15","doi-asserted-by":"crossref","unstructured":"Wang, K., Qin, C., Shi, Z., Wang, H., Zhang, X., Chen, C., Ouyang, C., Dai, C., Mo, Y., Dai, C., et\u00a0al.: Extreme cardiac mri analysis under respiratory motion: Results of the cmrxmotion challenge. arXiv preprint arXiv:2507.19165 (2025)","DOI":"10.1016\/j.media.2025.103883"},{"key":"7_CR16","unstructured":"Yang, K., Musio, F., Ma, Y., Juchler, N., Paetzold, J.C., Al-Maskari, R., H\u00f6her, L., Li, H.B., Hamamci, I.E., Sekuboyina, A., et\u00a0al.: Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra. ArXiv pp. arXiv\u20132312 (2025)"},{"issue":"12","key":"7_CR17","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","volume":"41","author":"X Zhuang","year":"2018","unstructured":"Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2933\u20132946 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101537","volume":"58","author":"X Zhuang","year":"2019","unstructured":"Zhuang, X., Li, L., Payer, C., \u0160tern, D., Urschler, M., Heinrich, M.P., Oster, J., Wang, C., Smedby, \u00d6., Bian, C., et al.: Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med. Image Anal. 58, 101537 (2019)","journal-title":"Med. Image Anal."},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.media.2016.02.006","volume":"31","author":"X Zhuang","year":"2016","unstructured":"Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of mri. Med. Image Anal. 31, 77\u201387 (2016)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Comprehensive Analysis and Computing of Real-World Medical Images"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16271-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T23:21:27Z","timestamp":1781479287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16271-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032162700","9783032162717"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16271-7_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":"1 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CARE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Challenge on Comprehensive Analysis and Computing of Real-World Medical Images","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"care-12025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/zmic.org.cn\/care_2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}