{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:59:48Z","timestamp":1790305188005,"version":"4.1.0"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032382351","type":"print"},{"value":"9783032382368","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-38236-8_37","type":"book-chapter","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:44:34Z","timestamp":1790304274000},"page":"383-392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RaD-Seg: Region-Aware Disentangled Learning for\u00a0Plan-Guided Longitudinal Tumor Segmentation in\u00a0Head-and-Neck Adaptive Radiotherapy"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4935-7815","authenticated-orcid":false,"given":"Hee Guan","family":"Khor","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1926-3807","authenticated-orcid":false,"given":"Fang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9324-3172","authenticated-orcid":false,"given":"Yingni","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6267-2177","authenticated-orcid":false,"given":"Longfei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3847-9347","authenticated-orcid":false,"given":"Hongen","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,26]]},"reference":[{"key":"37_CR1","doi-asserted-by":"crossref","unstructured":"Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET\/CT images. In: 3D Head and Neck Tumor Segmentation in PET\/CT Challenge, pp. 1\u201337. Springer (2021)","DOI":"10.1007\/978-3-030-98253-9_1"},{"issue":"8","key":"37_CR2","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"37_CR3","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":"37_CR4","doi-asserted-by":"crossref","unstructured":"K\u00e4chele, J., Zenk, M., Rokuss, M., Ulrich, C., Wald, T., Maier-Hein, K.H.: Enhanced nnU-Net architectures for automated MRI segmentation of head and neck tumors in adaptive radiation therapy. In: Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, pp. 50\u201364. Springer (2024)","DOI":"10.1007\/978-3-031-83274-1_3"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Khor, H.G., Chen, F., Liao, H.: CFM-SAM2: CNN-prompt-guided segment anything 2 with cross-modal fusion and contextual feature assimilation for gross target volume segmentation in nasopharyngeal carcinoma. In: Proceedings of the 2025 2nd International Conference on Smart Healthcare and Wearable Intelligent Devices, pp. 149\u2013154 (2025)","DOI":"10.1145\/3788112.3788135"},{"key":"37_CR6","doi-asserted-by":"publisher","unstructured":"Khor, H.G., et al.: RT-SAM: visual-prompt fusion and uncertainty enhancement for nasopharyngeal carcinoma radiotherapy target delineation. IEEE J. Biomed. Health Inform. 30(9), 7586\u20137599 (2026). https:\/\/doi.org\/10.1109\/JBHI.2026.3669979","DOI":"10.1109\/JBHI.2026.3669979"},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Khor, H.G., et al.: Unified prompt-visual interactive segmentation of clinical target volume in CT for nasopharyngeal carcinoma with prior anatomical information. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 659\u2013669. Springer (2024)","DOI":"10.1007\/978-3-031-72114-4_63"},{"key":"37_CR8","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.radonc.2022.11.004","volume":"177","author":"Z Li","year":"2022","unstructured":"Li, Z., et al.: Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy. Radiother. Oncol. 177, 222\u2013230 (2022)","journal-title":"Radiother. Oncol."},{"issue":"1","key":"37_CR9","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1002\/mp.16582","volume":"51","author":"BA McDonald","year":"2024","unstructured":"McDonald, B.A., et al.: Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers. Med. Phys. 51(1), 278\u2013291 (2024)","journal-title":"Med. Phys."},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Ren, J., Hochreuter, K., Kallehauge, J.F., Korreman, S.S.: UMamba adjustment: advancing GTV segmentation for head and neck cancer in MRI-guided RT with UMamba and nnU-Net ResEnc planner. In: Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, pp. 123\u2013135. Springer (2024)","DOI":"10.1007\/978-3-031-83274-1_9"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Ren, J., Hochreuter, K., Rasmussen, M.E., Kallehauge, J.F., Korreman, S.S.: Gradient map-assisted head and neck tumor segmentation: a Pre-RT to Mid-RT approach in MRI-guided radiotherapy. In: Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, pp. 36\u201349. Springer (2024)","DOI":"10.1007\/978-3-031-83274-1_2"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Tie, X., Chen, W., Huemann, Z., Schott, B., Liu, N., Bradshaw, T.J.: Deep learning for longitudinal gross tumor volume segmentation in MRI-guided adaptive radiotherapy for head and neck cancer. In: Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, pp. 99\u2013111. Springer (2024)","DOI":"10.1007\/978-3-031-83274-1_7"},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Vizitiu, A., Mohaiu, A.T., Popdan, I.M., Balachandran, A., Ghesu, F.C., Comaniciu, D.: Multi-scale self-supervised learning for longitudinal lesion tracking with optional supervision. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 573\u2013582. Springer (2023)","DOI":"10.1007\/978-3-031-43907-0_55"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Wahid, K.A., et al.: Overview of the head and neck tumor segmentation for magnetic resonance guided applications (HNTS-MRG) 2024 challenge. In: Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, pp. 1\u201335. Springer (2024)","DOI":"10.1007\/978-3-031-83274-1_1"},{"key":"37_CR15","doi-asserted-by":"crossref","unstructured":"Wang, L., Liao, W., Zhang, S., Wang, G.: Head and neck tumor segmentation of MRI from pre-and mid-radiotherapy with pre-training, data augmentation and dual flow UNet. In: Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, pp. 75\u201386. Springer (2024)","DOI":"10.1007\/978-3-031-83274-1_5"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38236-8_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T02:44:37Z","timestamp":1790304277000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38236-8_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,26]]},"ISBN":["9783032382351","9783032382368"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38236-8_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,26]]},"assertion":[{"value":"26 September 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","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}