{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:19:34Z","timestamp":1743005974087,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"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_6","type":"book-chapter","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T14:04:33Z","timestamp":1719842673000},"page":"63-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semi-supervised Two-Stage Abdominal Organ and\u00a0Tumor Segmentation Model with\u00a0Pseudo-labeling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5383-379X","authenticated-orcid":false,"given":"Li","family":"Mao","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"6_CR1","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"},{"issue":"6","key":"6_CR2","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1007\/s00371-022-02414-4","volume":"39","author":"H Xiao","year":"2023","unstructured":"Xiao, H., Ran, Z., Mabu, S., Li, Y., Li, L.: SAUNet++: an automatic segmentation model of Covid-19 lesion from CT slices. Vis. Comput. 39(6), 2291\u20132304 (2023)","journal-title":"Vis. Comput."},{"issue":"5","key":"6_CR3","doi-asserted-by":"publisher","first-page":"3027","DOI":"10.1002\/mp.16135","volume":"50","author":"S Pan","year":"2023","unstructured":"Pan, S., et al.: Abdomen CT multi-organ segmentation using token-based MLP-mixer. Med. Phys. 50(5), 3027\u20133038 (2023)","journal-title":"Med. Phys."},{"key":"6_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106501","volume":"158","author":"J Li","year":"2023","unstructured":"Li, J., et al.: Eres-UNet++: liver CT image segmentation based on high-efficiency channel attention and Res-UNet++. Comput. Biol. Med. 158, 106501 (2023)","journal-title":"Comput. Biol. Med."},{"key":"6_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102797","volume":"86","author":"F Bougourzi","year":"2023","unstructured":"Bougourzi, F., Distante, C., Dornaika, F., Taleb-Ahmed, A.: PDAtt-UNet: pyramid dual-decoder attention UNet for Covid-19 infection segmentation from CT-scans. Med. Image Anal. 86, 102797 (2023)","journal-title":"Med. Image Anal."},{"key":"6_CR6","first-page":"1","volume":"19","author":"L Zhang","year":"2022","unstructured":"Zhang, L., Lu, W., Zhang, J., Wang, H.: A semisupervised convolution neural network for partial unlabeled remote-sensing image segmentation. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"6_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101938","volume":"91","author":"O Petit","year":"2021","unstructured":"Petit, O., Thome, N., Soler, L.: Iterative confidence relabeling with deep convnets for organ segmentation with partial labels. Comput. Med. Imaging Graph. 91, 101938 (2021)","journal-title":"Comput. Med. Imaging Graph."},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Li, L., Lian, S., Lin, D., Luo, Z., Wang, B., Li, S.: Learning multi-organ and tumor segmentation from partially labeled datasets by a conditional dynamic attention network. Concurr. Comput. Pract. Experience e7869 (2023)","DOI":"10.1002\/cpe.7869"},{"issue":"1","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2014.10.012","volume":"20","author":"E Smistad","year":"2015","unstructured":"Smistad, E., Falch, T.L., Bozorgi, M., Elster, A.C., Lindseth, F.: Medical image segmentation on GPUs-a comprehensive review. Med. Image Anal. 20(1), 1\u201318 (2015)","journal-title":"Med. Image Anal."},{"key":"6_CR10","unstructured":"Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: a deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147 (2016)"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"4873","DOI":"10.1109\/TIP.2020.2976689","volume":"29","author":"G Li","year":"2020","unstructured":"Li, G., Liu, Z., Ling, H.: ICNet: information conversion network for RGB-D based salient object detection. IEEE Trans. Image Process. 29, 4873\u20134884 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Molchanov, P., Mallya, A., Tyree, S., Frosio, I., Kautz, J.: Importance estimation for neural network pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11264\u201311272 (2019)","DOI":"10.1109\/CVPR.2019.01152"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"6_CR14","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":"6_CR15","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":"6_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-031-23911-3_16","volume-title":"Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation","author":"Z Huang","year":"2022","unstructured":"Huang, Z., et al.: Revisiting nnU-net for iterative pseudo labeling and efficient sliding window inference. In: Ma, J., Wang, B. (eds.) FLARE 2022. LNCS, vol. 13816, pp. 178\u2013189. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-23911-3_16"},{"key":"6_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1007\/978-3-031-23911-3_18","volume-title":"Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation","author":"E Wang","year":"2022","unstructured":"Wang, E., Zhao, Y., Wu, Y.: Cascade dual-decoders network for abdominal organs segmentation. In: Ma, J., Wang, B. (eds.) FLARE 2022. LNCS, vol. 13816, pp. 202\u2013213. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-23911-3_18"},{"key":"6_CR18","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":"6_CR19","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":"6","key":"6_CR20","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":"6_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). Med. Image Anal. 84, 102680 (2023)","journal-title":"Med. Image Anal."},{"key":"6_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":"6_CR23","doi-asserted-by":"publisher","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":"6_CR24","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 (2020)","journal-title":"Proc. Am. Soc. Clin. Oncol."},{"issue":"1","key":"6_CR25","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":"6_CR26","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":"10","key":"6_CR27","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":"6_CR28","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":"6_CR29","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."},{"issue":"198","key":"6_CR30","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."}],"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_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T05:36:52Z","timestamp":1732340212000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58776-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031587757","9783031587764"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58776-4_6","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"}}]}}