{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:01:58Z","timestamp":1780617718440,"version":"3.54.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721106","type":"print"},{"value":"9783031721113","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72111-3_35","type":"book-chapter","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:01:34Z","timestamp":1728162094000},"page":"371-381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Low-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in\u00a0CT with\u00a0Light-Weighted Adaptation"],"prefix":"10.1007","author":[{"given":"Vince","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanghexuan","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dazhou","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Puyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingda","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghua","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dakai","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Cermelli, F., Mancini, M., Bulo, S.R., Ricci, E., Caputo, B.: Modeling the background for incremental learning in semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9233\u20139242 (2020)","DOI":"10.1109\/CVPR42600.2020.00925"},{"issue":"3","key":"35_CR2","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1038\/s42256-023-00626-4","volume":"5","author":"N Ding","year":"2023","unstructured":"Ding, N., et al.: Parameter-efficient fine-tuning of large-scale pre-trained language models. Nat. Mach. Intell. 5(3), 220\u2013235 (2023)","journal-title":"Nat. Mach. Intell."},{"key":"35_CR3","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Douillard, A., Chen, Y., Dapogny, A., Cord, M.: Plop: learning without forgetting for continual semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4040\u20134050 (2021)","DOI":"10.1109\/CVPR46437.2021.00403"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Guo, D., et al.: Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4223\u20134232 (2020)","DOI":"10.1109\/CVPR42600.2020.00428"},{"key":"35_CR6","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: International Conference on Machine Learning, pp. 2790\u20132799. PMLR (2019)"},{"key":"35_CR7","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: International Conference on Learning Representations (2022)"},{"issue":"2","key":"35_CR8","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":"35_CR9","doi-asserted-by":"crossref","unstructured":"Ji, Z., et al.: Continual segment: towards a single, unified and non-forgetting continual segmentation model of 143 whole-body organs in CT scans. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21140\u201321151 (2023)","DOI":"10.1109\/ICCV51070.2023.01933"},{"issue":"4","key":"35_CR10","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.jncc.2022.09.003","volume":"2","author":"D Jin","year":"2022","unstructured":"Jin, D., Guo, D., Ge, J., Ye, X., Lu, L.: Towards automated organs at risk and target volumes contouring: defining precision radiation therapy in the modern era. J. Natl. Cancer Center 2(4), 306\u2013313 (2022)","journal-title":"J. Natl. Cancer Center"},{"key":"35_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101909","volume":"68","author":"D Jin","year":"2021","unstructured":"Jin, D., et al.: Deeptarget: gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy. Med. Image Anal. 68, 101909 (2021)","journal-title":"Med. Image Anal."},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Kemker, R., McClure, M., Abitino, A., Hayes, T., Kanan, C.: Measuring catastrophic forgetting in neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11651"},{"key":"35_CR13","unstructured":"Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"35_CR14","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1007\/978-3-031-16440-8_68","volume-title":"MICCAI 2022","author":"P Liu","year":"2022","unstructured":"Liu, P., et al.: Learning incrementally to segment multiple organs in a CT image. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13434, pp. 714\u2013724. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16440-8_68"},{"key":"35_CR15","unstructured":"Ma, C., Ji, Z., Huang, Z., Shen, Y., Gao, M., Xu, J.: Progressive voronoi diagram subdivision enables accurate data-free class-incremental learning. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"35_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-030-00937-3_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"F Ozdemir","year":"2018","unstructured":"Ozdemir, F., Fuernstahl, P., Goksel, O.: Learn the new, keep the old: extending pretrained models with new anatomy and images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 361\u2013369. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_42"},{"key":"35_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-030-59710-8_45","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"A Raju","year":"2020","unstructured":"Raju, A., et al.: User-guided domain adaptation for rapid annotation from user interactions: a study on pathological liver segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 457\u2013467. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_45"},{"key":"35_CR18","unstructured":"Shen, Y., Ji, Z., Ma, C., Gao, M.: Continual domain adversarial adaptation via double-head discriminators. In: International Conference on Artificial Intelligence and Statistics, pp. 2584\u20132592. PMLR (2024)"},{"issue":"1","key":"35_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-34257-x","volume":"13","author":"F Shi","year":"2022","unstructured":"Shi, F., et al.: Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy. Nat. Commun. 13(1), 1\u201313 (2022)","journal-title":"Nat. Commun."},{"issue":"10","key":"35_CR20","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1038\/s42256-019-0099-z","volume":"1","author":"H Tang","year":"2019","unstructured":"Tang, H., et al.: Clinically applicable deep learning framework for organs at risk delineation in CT images. Nat. Mach. Intell. 1(10), 480\u2013491 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: Self-supervised pre-training of swin transformers for 3D medical image analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730\u201320740 (2022)","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Wang, P., et al.: Accurate airway tree segmentation in CT scans via anatomy-aware multi-class segmentation and topology-guided iterative learning. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3419707"},{"key":"35_CR23","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Wasserthal, J., Meyer, M., Breit, H.C., Cyriac, J., Yang, S., Segeroth, M.: Totalsegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv preprint arXiv:2208.05868 (2022)","DOI":"10.1148\/ryai.230024"},{"key":"35_CR25","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1007\/978-3-031-19803-8_33","volume-title":"ECCV 2022","author":"Y Xie","year":"2022","unstructured":"Xie, Y., Zhang, J., Xia, Y., Wu, Q.: UniMiSS: universal medical self-supervised learning via breaking dimensionality barrier. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13681, pp. 558\u2013575. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19803-8_33"},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"Ye, X., et al.: Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study. Nat. Commun. 13(1), 1\u201315 (2022)","DOI":"10.1038\/s41467-022-33178-z"},{"key":"35_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, C.B., Xiao, J.W., Liu, X., Chen, Y.C., Cheng, M.M.: Representation compensation networks for continual semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7053\u20137064 (2022)","DOI":"10.1109\/CVPR52688.2022.00692"},{"key":"35_CR28","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-031-43895-0_4","volume-title":"MICCAI 2023","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Li, X., Chen, H., Yuille, A.L., Liu, Y., Zhou, Z.: Continual learning for abdominal multi-organ and tumor segmentation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14221, pp. 35\u201345. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_4"},{"key":"35_CR29","unstructured":"Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., Yu, Y.: nnFormer: interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201 (2021)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72111-3_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:05:01Z","timestamp":1728162301000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72111-3_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721106","9783031721113"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72111-3_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 October 2024","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":"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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}