{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:36:03Z","timestamp":1742927763507,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031587757"},{"type":"electronic","value":"9783031587764"}],"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\/978-3-031-58776-4_17","type":"book-chapter","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T14:04:33Z","timestamp":1719842673000},"page":"209-221","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Selected Partially Labeled Learning for\u00a0Abdominal Organ and\u00a0Pan-Cancer 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\/0009-0001-2630-1683","authenticated-orcid":false,"given":"Linyao","family":"Li","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"}]}],"member":"297","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"17_CR1","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (lits). Med. Image Anal. 84, 102680 (2023)"},{"issue":"6","key":"17_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"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Dmitriev, K., Kaufman, A.E.: Learning multi-class segmentations from single-class datasets. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00973"},{"issue":"11","key":"17_CR4","doi-asserted-by":"publisher","first-page":"3619","DOI":"10.1109\/TMI.2020.3001036","volume":"39","author":"X Fang","year":"2020","unstructured":"Fang, X., Yan, P.: Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Trans. Med. Imaging 39(11), 3619\u20133629 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Fidon, L., et\u00a0al.: Label-set loss functions for partial supervision: application to fetal brain 3d MRI parcellation. In: Medical Image Computing and Computer Assisted Intervention (2021)","DOI":"10.1007\/978-3-030-87196-3_60"},{"key":"17_CR6","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":"17_CR7","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":"17_CR8","doi-asserted-by":"publisher","first-page":"101821","DOI":"10.1016\/j.media.2020.101821","volume":"67","author":"N Heller","year":"2021","unstructured":"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":"17_CR9","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":"17_CR10","doi-asserted-by":"publisher","unstructured":"Huang, Z., et al.: Revisiting nnU-net for iterative pseudo labeling and efficient sliding window inference. In: Ma, J., Wang, B. (eds.) Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, FLARE 2022, LNCS, vol. 13816, pp. 178\u2013189. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-23911-3_16","DOI":"10.1007\/978-3-031-23911-3_16"},{"issue":"2","key":"17_CR11","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":"17_CR12","doi-asserted-by":"crossref","unstructured":"Li, S., Wang, H., Meng, Y., Zhang, C., Song, Z.: Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation. arXiv preprint arXiv:2302.03296 (2023)","DOI":"10.1088\/1361-6560\/ad33b5"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Clip-driven universal model for organ segmentation and tumor detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21152\u201321164 (2023)","DOI":"10.1109\/ICCV51070.2023.01934"},{"key":"17_CR14","doi-asserted-by":"publisher","first-page":"102642","DOI":"10.1016\/j.media.2022.102642","volume":"82","author":"X Luo","year":"2022","unstructured":"Luo, X., et al.: Word: a large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image. Med. Image Anal. 82, 102642 (2022)","journal-title":"Med. Image Anal."},{"key":"17_CR15","doi-asserted-by":"publisher","first-page":"102035","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."},{"issue":"1","key":"17_CR16","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(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"102616","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":"17_CR18","doi-asserted-by":"crossref","unstructured":"Ma, J., et al.: Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge. arXiv preprint arXiv:2308.05862 (2023)","DOI":"10.1016\/S2589-7500(24)00154-7"},{"issue":"10","key":"17_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."},{"issue":"198","key":"17_CR20","first-page":"1","volume":"24","author":"A Pavao","year":"2023","unstructured":"Pavao, A., et al.: Codalab competitions: an open source platform to organize scientific challenges. J. Mach. Learn. Res. 24(198), 1\u20136 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR21","unstructured":"Roulet, N., Slezak, D.F., Ferrante, E.: Joint learning of brain lesion and anatomy segmentation from heterogeneous datasets. In: Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning (2019)"},{"key":"17_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101979","volume":"70","author":"G Shi","year":"2021","unstructured":"Shi, G., Xiao, L., Chen, Y., Zhou, S.K.: Marginal loss and exclusion loss for partially supervised multi-organ segmentation. Med. Image Anal. 70, 101979 (2021)","journal-title":"Med. Image Anal."},{"key":"17_CR23","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":"17_CR24","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: Self-supervised pre-training of swin transformers for 3d medical image analysis. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Ulrich, C., Isensee, F., Wald, T., Zenk, M., Baumgartner, M., Maier-Hein, K.H.: Multitalent: a multi-dataset approach to medical image segmentation. arXiv preprint arXiv:2303.14444 (2023)","DOI":"10.1007\/978-3-031-43898-1_62"},{"issue":"4","key":"17_CR26","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1109\/TMI.2022.3225667","volume":"42","author":"C Wang","year":"2023","unstructured":"Wang, C., Cui, Z., Yang, J., Han, M., Carneiro, G., Shen, D.: Bowelnet: joint semantic-geometric ensemble learning for bowel segmentation from both partially and fully labeled CT images. IEEE Trans. Med. Imaging 42(4), 1225\u20131236 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"17_CR27","doi-asserted-by":"publisher","unstructured":"Wang, E., Zhao, Y., Wu, Y.: Cascade dual-decoders network for abdominal organs segmentation. In: Ma, J., Wang, B. (eds.) Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, FLARE 2022, LNCS, vol. 13816, pp. 202\u2013213. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-23911-3_18","DOI":"10.1007\/978-3-031-23911-3_18"},{"issue":"5","key":"17_CR28","doi-asserted-by":"publisher","first-page":"e230024","DOI":"10.1148\/ryai.230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J., et al.: Totalsegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5(5), e230024 (2023)","journal-title":"Radiol. Artif. Intell."},{"key":"17_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":"17_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xie, Y., Xia, Y., Shen, C.: Dodnet: learning to segment multi-organ and tumors from multiple partially labeled datasets. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021","DOI":"10.1109\/CVPR46437.2021.00125"},{"key":"17_CR31","doi-asserted-by":"publisher","first-page":"101840","DOI":"10.1016\/j.media.2020.101840","volume":"67","author":"Z Zhou","year":"2021","unstructured":"Zhou, Z., Sodha, V., Pang, J., Gotway, M.B., Liang, J.: Models genesis. Med. Image Anal. 67, 101840 (2021)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58776-4_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T05:37:38Z","timestamp":1732340258000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58776-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031587757","9783031587764"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58776-4_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"flare2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/codalab.lisn.upsaclay.fr\/competitions\/12239","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}