{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:23:57Z","timestamp":1757618637957,"version":"3.44.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031962011"},{"type":"electronic","value":"9783031962028"}],"license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"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\/978-3-031-96202-8_14","type":"book-chapter","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:22:34Z","timestamp":1752139354000},"page":"164-177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Pseudo Label-Based Unsupervised Multiple Target Domain Adaptation Framework for\u00a0Abdominal Organ Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2816-2709","authenticated-orcid":false,"given":"Yuntao","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4085-4003","authenticated-orcid":false,"given":"Liwen","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5211-4876","authenticated-orcid":false,"given":"Pengxu","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwei","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luying","family":"Gui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoping","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"14_CR1","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (lits). Medical Image Analysis 84, 102680 (2023)"},{"issue":"6","key":"14_CR2","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045\u20131057 (2013)","journal-title":"J. Digit. Imaging"},{"issue":"9","key":"14_CR3","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323\u20131341 (2012)","journal-title":"Magn. Reson. Imaging"},{"key":"14_CR4","doi-asserted-by":"publisher","unstructured":"Gatidis, S., et\u00a0al.: The autopet challenge: towards fully automated lesion segmentation in oncologic PET\/CT imaging. preprint at Research Square (Nature Portfolio ) (2023). https:\/\/doi.org\/10.21203\/rs.3.rs-2572595\/v1","DOI":"10.21203\/rs.3.rs-2572595\/v1"},{"issue":"1","key":"14_CR5","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1038\/s41597-022-01718-3","volume":"9","author":"S Gatidis","year":"2022","unstructured":"Gatidis, S., et al.: A whole-body FDG-PET\/CT dataset with manually annotated tumor lesions. Sci. Data 9(1), 601 (2022)","journal-title":"Sci. Data"},{"key":"14_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101821","volume":"67","author":"N Heller","year":"2021","unstructured":"Heller, N., Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the kits19 challenge. Med. Image Anal. 67, 101821 (2021)","journal-title":"Med. Image Anal."},{"issue":"6","key":"14_CR7","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1200\/JCO.2020.38.6_suppl.626","volume":"38","author":"N Heller","year":"2020","unstructured":"Heller, N., et al.: An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in ct imaging. Proc. Am. Soc. Clin. Oncol. 38(6), 626\u2013626 (2020)","journal-title":"Proc. Am. Soc. Clin. Oncol."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Huang, Z., et al.: Revisiting nnu-net for iterative pseudo labeling and efficient sliding window inference. In: MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, pp. 178\u2013189. Springer (2022)","DOI":"10.1007\/978-3-031-23911-3_16"},{"issue":"2","key":"14_CR9","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":"14_CR10","unstructured":"Ji, Y., et al.: Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation. Advances Neural Inform. Process. Syst. 35, 36722\u201336732 (2022)"},{"key":"14_CR11","unstructured":"Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: NeurIPS, pp. 136\u2013144 (2016)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Lou, M., Ying, H., Liu, X., Zhou, H.Y., Zhang, Y., Yu, Y.: SDR-Former: a siamese dual-resolution transformer for liver lesion classification using 3d multi-phase imaging. arXiv preprint arXiv:2402.17246 (2024)","DOI":"10.1016\/j.neunet.2025.107228"},{"key":"14_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102035","volume":"71","author":"J Ma","year":"2021","unstructured":"Ma, J., et al.: Loss odyssey in medical image segmentation. Med. Image Anal. 71, 102035 (2021)","journal-title":"Med. Image Anal."},{"key":"14_CR14","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15, 654 (2024)","journal-title":"Nat. Commun."},{"key":"14_CR15","unstructured":"Ma, J., et al.: Segment anything in medical images and videos: Benchmark and deployment. arXiv preprint arXiv:2408.03322 (2024)"},{"key":"14_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102616","volume":"82","author":"J Ma","year":"2022","unstructured":"Ma, J., et al.: Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge. Med. Image Anal. 82, 102616 (2022)","journal-title":"Med. Image Anal."},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Ma, J., et al.: Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge. Lancet Digital Health (2024)","DOI":"10.1016\/S2589-7500(24)00154-7"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Ma, J., et al.: Automatic organ and pan-cancer segmentation in abdomen CT: the flare 2023 challenge. arXiv preprint arXiv:2408.12534 (2024)","DOI":"10.1007\/978-3-031-58776-4"},{"issue":"10","key":"14_CR19","doi-asserted-by":"publisher","first-page":"6695","DOI":"10.1109\/TPAMI.2021.3100536","volume":"44","author":"J Ma","year":"2022","unstructured":"Ma, J., et al.: Abdomenct-1k: Is abdominal organ segmentation a solved problem? IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6695\u20136714 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Park, T., Efros, A.A., Zhang, R., Zhu, J.Y.: Contrastive learning for unpaired image-to-image translation. In: ECCV, pp. 319\u2013345 (2020)","DOI":"10.1007\/978-3-030-58545-7_19"},{"issue":"2","key":"14_CR21","first-page":"63","volume":"36","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., Shen, C., Oda, H., Oda, M., et al.: Deep learning and its application to medical image segmentation. Med. Imag. Technol. 36(2), 63\u201371 (2018)","journal-title":"Med. Imag. Technol."},{"key":"14_CR22","unstructured":"Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Wang, E., Zhao, Y., Wu, Y.: Cascade dual-decoders network for abdominal organs segmentation. In: MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, pp. 202\u2013213. Springer (2022)","DOI":"10.1007\/978-3-031-23911-3_18"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Wasserthal, J., et al.: Totalsegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiol.: Artif. Intell. 5(5), e230024 (2023)","DOI":"10.1148\/ryai.230024"},{"key":"14_CR25","doi-asserted-by":"publisher","unstructured":"Wu, J., et al.: FPL+: Filtered pseudo label-based unsupervised cross-modality adaptation for 3D medical image segmentation. IEEE Trans. Med. Imaging (2024). https:\/\/doi.org\/10.1109\/TMI.2024.3387415","DOI":"10.1109\/TMI.2024.3387415"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Wu, J., et\u00a0al.: TISS-Net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency. Neurocomputing 126295 (2023)","DOI":"10.1016\/j.neucom.2023.126295"},{"issue":"7","key":"14_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100543","volume":"3","author":"Z Xu","year":"2022","unstructured":"Xu, Z., et al.: Codabench: flexible, easy-to-use, and reproducible meta-benchmark platform. Patterns 3(7), 100543 (2022)","journal-title":"Patterns"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Yao, K., Su, Z., Huang, K., Coenen, F.: A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation. IEEE J. Biomed. Health Inform. 26(10) (2022)","DOI":"10.1109\/JBHI.2022.3162118"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Yushkevich, P.A., Gao, Y., Gerig, G.: ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3342\u20133345 (2016)","DOI":"10.1109\/EMBC.2016.7591443"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96202-8_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T04:17:16Z","timestamp":1757218636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96202-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"ISBN":["9783031962011","9783031962028"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96202-8_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"9 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"FLARE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation","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":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"flare2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.codabench.org\/competitions\/2319\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}