{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:05:53Z","timestamp":1769846753220,"version":"3.49.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439926","type":"print"},{"value":"9783031439933","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43993-3_1","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["CoactSeg: Learning from\u00a0Heterogeneous Data for\u00a0New Multiple Sclerosis Lesion Segmentation"],"prefix":"10.1007","author":[{"given":"Yicheng","family":"Wu","sequence":"first","affiliation":[]},{"given":"Zhonghua","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Hengcan","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Bjoern","family":"Picker","sequence":"additional","affiliation":[]},{"given":"Winston","family":"Chong","sequence":"additional","affiliation":[]},{"given":"Jianfei","family":"Cai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Carass, A., et al.: Longitudinal multiple sclerosis lesion segmentation: resource and challenge. NeuroImage 148, 77\u2013102 (2017)","DOI":"10.1016\/j.neuroimage.2016.12.064"},{"key":"1_CR2","unstructured":"Commowick, O., Cervenansky, F., Cotton, F., Dojat, M.: Msseg-2 challenge proceedings: multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure. In: MICCAI 2021, p. 126 (2021)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Commowick, O., et al.: Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure. Sci. Rep. 8(1), 13650 (2018)","DOI":"10.1038\/s41598-018-31911-7"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Commowick, O., et al.: Multiple sclerosis lesions segmentation from multiple experts: the MICCAI 2016 challenge dataset. Neuroimage 244, 118589 (2021)","DOI":"10.1016\/j.neuroimage.2021.118589"},{"key":"1_CR5","unstructured":"Gessert, N., et al.: 4d deep learning for multiple-sclerosis lesion activity segmentation. In: MIDL 2020 (2020)"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Gessert, N., et al.: Multiple sclerosis lesion activity segmentation with attention-guided two-path CNNs. Computer. Med. Imaging Graph. 84, 101772 (2020)","DOI":"10.1016\/j.compmedimag.2020.101772"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Gold, R., et al.: Placebo-controlled phase 3 study of oral bg-12 for relapsing multiple sclerosis. N. Engl. J. Med. 367(12), 1098\u20131107 (2012)","DOI":"10.1056\/NEJMoa1114287"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"He, T., et al.: MS or not MS: T2-weighted imaging (t2wi)-based radiomic findings distinguish MS from its mimics. Multip. Sclerosis Relat. Disord. 61, 103756 (2022)","DOI":"10.1016\/j.msard.2022.103756"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Kr\u00fcger, J., et al.: Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3d convolutional neural networks. NeuroImage: Clin. 28, 102445 (2020)","DOI":"10.1016\/j.nicl.2020.102445"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"La Rosa, F., et al.: Multiple sclerosis cortical and WM lesion segmentation at 3t MRI: a deep learning method based on flair and mp2rage. NeuroImage: Clin. 27, 102335 (2020)","DOI":"10.1016\/j.nicl.2020.102335"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Ma, Y., et al.: Multiple sclerosis lesion analysis in brain magnetic resonance images: techniques and clinical applications. IEEE J. Biomed. Health Inf. 26(6), 2680\u20132692 (2022)","DOI":"10.1109\/JBHI.2022.3151741"},{"key":"1_CR12","unstructured":"Macar, U., Karthik, E.N., Gros, C., Lemay, A., Cohen-Adad, J.: Team neuropoly: description of the pipelines for the MICCAI 2021 MS new lesions segmentation challenge. arXiv preprint arXiv:2109.05409 (2021)"},{"key":"1_CR13","unstructured":"Maier-Hein, L., et al.: Metrics reloaded: pitfalls and recommendations for image analysis validation. arXiv preprint arXiv:2206.01653 (2022)"},{"key":"1_CR14","unstructured":"Mariano, C., Yuling, L., Kain, K., Linda, L., Chenyu, W., Michael, B.: Estimating lesion activity through feature similarity: a dual path UNET approach for the msseg2 MICCAI challenge. https:\/\/github.com\/marianocabezas\/msseg2"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV 2016, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Raki\u0107, M., et al.: icobrain MS 5.1: combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions. NeuroImage: Clin. 31, 102707 (2021)","DOI":"10.1016\/j.nicl.2021.102707"},{"issue":"4","key":"1_CR17","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.1016\/j.neuroimage.2012.02.084","volume":"61","author":"M Reuter","year":"2012","unstructured":"Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402\u20131418 (2012)","journal-title":"Neuroimage"},{"key":"1_CR18","unstructured":"Schell, M., et al.: Automated brain extraction of multi-sequence MRI using artificial neural networks. In: Human Brain Mapping, pp. 1\u201313 (2019)"},{"key":"1_CR19","unstructured":"Sharmin, S., et al.: Confirmed disability progression as a marker of permanent disability in multiple sclerosis. Eur. J. Neurol. 29(8), 2321\u20132334 (2022)"},{"key":"1_CR20","doi-asserted-by":"publisher","unstructured":"Tang, Z., Cabezas, M., Liu, D., Barnett, M., Cai, W., Wang, C.: LG-net: lesion gate network for multiple sclerosis lesion inpainting. In: de Bruijne, M. et al. (eds.) MICCAI 2021, pp. 660\u2013669. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_62","DOI":"10.1007\/978-3-030-87234-2_62"},{"key":"1_CR21","doi-asserted-by":"publisher","unstructured":"Wolleb, J., et al.: Learn to ignore: domain adaptation for multi-site MRI analysis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, pp. 725\u2013735. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_69","DOI":"10.1007\/978-3-031-16449-1_69"},{"key":"1_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102530","volume":"81","author":"Y Wu","year":"2022","unstructured":"Wu, Y., Ge, Z., Zhang, D., Xu, M., Zhang, L., Xia, Y., Cai, J.: Mutual consistency learning for semi-supervised medical image segmentation. Med. Image Anal. 81, 102530 (2022)","journal-title":"Med. Image Anal."},{"key":"1_CR23","doi-asserted-by":"publisher","unstructured":"Wu, Y., Wu, Z., Wu, Q., Ge, Z., Cai, J.: Exploring smoothness and class-separation for semi-supervised medical image segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, vol. 13435, pp. 34\u201343. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_4","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"1_CR24","doi-asserted-by":"publisher","unstructured":"Wu, Y., Xu, M., Ge, Z., Cai, J., Zhang, L.: Semi-supervised left atrium segmentation with mutual consistency training. In: de Bruijne, M., et al. (eds.) MICCAI 2021, pp. 297\u2013306. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_28","DOI":"10.1007\/978-3-030-87196-3_28"},{"key":"1_CR25","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2020.610967","volume":"14","author":"C Zeng","year":"2020","unstructured":"Zeng, C., Gu, L., Liu, Z., Zhao, S.: Review of deep learning approaches for the segmentation of multiple sclerosis lesions on brain MRI. Front. Neuroinform. 14, 610967 (2020)","journal-title":"Front. Neuroinform."},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Qsmrim-net: imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps. NeuroImage: Clin. 34, 102979 (2022)","DOI":"10.1016\/j.nicl.2022.102979"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, R., Zhang, J., Liu, D., Li, C., Li, J.: Spatially covariant lesion segmentation. arXiv preprint arXiv:2301.07895 (2023)","DOI":"10.24963\/ijcai.2023\/190"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: All-net: anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation. NeuroImage: Clin. 32, 102854 (2021)","DOI":"10.1016\/j.nicl.2021.102854"},{"key":"1_CR29","unstructured":"Zhang, H., Yuan, X., Nguyen, Q.V.H., Pan, S.: On the interaction between node fairness and edge privacy in graph neural networks. arXiv preprint arXiv:2301.12951 (2023)"},{"key":"1_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: CVPR 2021, pp. 1195\u20131204 (2021)","DOI":"10.1109\/CVPR46437.2021.00125"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43993-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:07:54Z","timestamp":1712074074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"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":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"730","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}