{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:51:42Z","timestamp":1781614302629,"version":"3.54.5"},"reference-count":29,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82072021"],"award-info":[{"award-number":["82072021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.bspc.2026.110174","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T09:46:29Z","timestamp":1774604789000},"page":"110174","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["HUR-MACL: High-uncertainty region-guided multi-architecture collaborative learning for head and neck multi-organ segmentation"],"prefix":"10.1016","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7338-8494","authenticated-orcid":false,"given":"Xiaoyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siwen","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhao","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyuan","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengsheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2268-2087","authenticated-orcid":false,"given":"Yonghong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijian","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"S7","key":"10.1016\/j.bspc.2026.110174_b1","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1002\/cncr.23654","article-title":"Head and neck cancer: An evolving treatment paradigm","volume":"113","author":"Cognetti","year":"2008","journal-title":"Cancer"},{"issue":"4","key":"10.1016\/j.bspc.2026.110174_b2","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1080\/14737140.2018.1446832","article-title":"An update on radiation therapy in head and neck cancers","volume":"18","author":"Mazzola","year":"2018","journal-title":"Expert. Rev. Anticancer. Ther."},{"key":"10.1016\/j.bspc.2026.110174_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2023.109574","article-title":"Benefits of automated gross tumor volume segmentation in head and neck cancer using multi-modality information","volume":"182","author":"Bollen","year":"2023","journal-title":"Radiother. Oncol."},{"key":"10.1016\/j.bspc.2026.110174_b4","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.radonc.2019.05.010","article-title":"Benefits of deep learning for delineation of organs at risk in head and neck cancer","volume":"138","author":"Van der Veen","year":"2019","journal-title":"Radiother. Oncol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110174_b5","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s12938-024-01238-8","article-title":"Towards more precise automatic analysis: A systematic review of deep learning-based multi-organ segmentation","volume":"23","author":"Liu","year":"2024","journal-title":"BioMed. Eng. OnLine"},{"issue":"8","key":"10.1016\/j.bspc.2026.110174_b6","doi-asserted-by":"crossref","first-page":"4074","DOI":"10.1109\/JBHI.2023.3275746","article-title":"MHL-net: A multistage hierarchical learning network for head and neck multiorgan segmentation","volume":"27","author":"Wang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"5","key":"10.1016\/j.bspc.2026.110174_b7","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1109\/TMI.2024.3354673","article-title":"Cosst: Multi-organ segmentation with partially labeled datasets using comprehensive supervisions and self-training","volume":"43","author":"Liu","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.bspc.2026.110174_b8","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/TMI.2021.3128408","article-title":"3D lightweight network for simultaneous registration and segmentation of organs-at-risk in CT images of head and neck cancer","volume":"41","author":"Huang","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110174_b9","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24","first-page":"569","article-title":"A novel hybrid convolutional neural network for accurate organ segmentation in 3D head and neck CT images","author":"Chen","year":"2021"},{"issue":"9","key":"10.1016\/j.bspc.2026.110174_b10","doi-asserted-by":"crossref","first-page":"2794","DOI":"10.1109\/TMI.2020.2975853","article-title":"Multi-view spatial aggregation framework for joint localization and segmentation of organs at risk in head and neck CT images","volume":"39","author":"Liang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.110174_b11","doi-asserted-by":"crossref","first-page":"6566","DOI":"10.1038\/s41467-022-34257-x","article-title":"Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy","volume":"13","author":"Shi","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2026.110174_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101831","article-title":"FocusNetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images","volume":"67","author":"Gao","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110174_b13","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"2","key":"10.1016\/j.bspc.2026.110174_b14","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"key":"10.1016\/j.bspc.2026.110174_b15","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.110174_b16","series-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2023"},{"key":"10.1016\/j.bspc.2026.110174_b17","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.bspc.2026.110174_b18","series-title":"Mamba-unet: Unet-like pure visual mamba for medical image segmentation","author":"Wang","year":"2024"},{"issue":"6","key":"10.1016\/j.bspc.2026.110174_b19","first-page":"549","article-title":"Advantages of hybrid deep learning frameworks in applications with limited data","volume":"8","author":"Gavrishchaka","year":"2018","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"10.1016\/j.bspc.2026.110174_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101874","article-title":"CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging","volume":"67","author":"Mou","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110174_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.107955","article-title":"CSSNet: Cascaded spatial shift network for multi-organ segmentation","volume":"170","author":"Shao","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110174_b22","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III 24","first-page":"61","article-title":"UTNet: A hybrid transformer architecture for medical image segmentation","author":"Gao","year":"2021"},{"key":"10.1016\/j.bspc.2026.110174_b23","unstructured":"L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, X. Wang, Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model, in: Proceedings of the 41st International Conference on Machine Learning, 2024, pp. 62429\u201362442."},{"key":"10.1016\/j.bspc.2026.110174_b24","doi-asserted-by":"crossref","unstructured":"J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, Y. Wei, Deformable convolutional networks, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 764\u2013773.","DOI":"10.1109\/ICCV.2017.89"},{"key":"10.1016\/j.bspc.2026.110174_b25","doi-asserted-by":"crossref","unstructured":"Y. Qi, Y. He, X. Qi, Y. Zhang, G. Yang, Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 6070\u20136079.","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"10.1016\/j.bspc.2026.110174_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102389","article-title":"Deu-net 2.0: Enhanced deformable U-net for 3D cardiac cine mri segmentation","volume":"78","author":"Dong","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110174_b27","series-title":"Deep mutual learning among partially labeled datasets for multi-organ segmentation","author":"Liu","year":"2024"},{"key":"10.1016\/j.bspc.2026.110174_b28","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.1016\/j.bspc.2026.110174_b29","doi-asserted-by":"crossref","unstructured":"J. Zhu, Y. Luo, X. Zheng, H. Wang, L. Wang, A good student is cooperative and reliable: Cnn-transformer collaborative learning for semantic segmentation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 11720\u201311730.","DOI":"10.1109\/ICCV51070.2023.01076"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426007287?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426007287?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T11:53:49Z","timestamp":1781610829000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426007287"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":29,"alternative-id":["S1746809426007287"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110174","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"HUR-MACL: High-uncertainty region-guided multi-architecture collaborative learning for head and neck multi-organ segmentation","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110174","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110174"}}