{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T09:49:09Z","timestamp":1743846549256,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030597184"},{"type":"electronic","value":"9783030597191"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59719-1_52","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T18:02:56Z","timestamp":1601661776000},"page":"533-541","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Brain Tumor Segmentation with Missing Modalities via Latent Multi-source Correlation Representation"],"prefix":"10.1007","author":[{"given":"Tongxue","family":"Zhou","sequence":"first","affiliation":[]},{"given":"St\u00e9phane","family":"Canu","sequence":"additional","affiliation":[]},{"given":"Pierre","family":"Vera","sequence":"additional","affiliation":[]},{"given":"Su","family":"Ruan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"52_CR1","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight J 2, 1\u201335 (2009)","journal-title":"Insight J"},{"issue":"3","key":"52_CR2","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1109\/TMI.2017.2764326","volume":"37","author":"A Chartsias","year":"2017","unstructured":"Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.: Multimodal MR synthesis via modality-invariant latent representation. IEEE Trans. Med. Imaging 37(3), 803\u2013814 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"52_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/978-3-030-39074-7_14","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges","author":"A Chartsias","year":"2020","unstructured":"Chartsias, A., et al.: Multimodal cardiac segmentation using disentangled representation learning. In: Pop, M., et al. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 128\u2013137. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-39074-7_14"},{"key":"52_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/978-3-030-32248-9_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"C Chen","year":"2019","unstructured":"Chen, C., Dou, Q., Jin, Y., Chen, H., Qin, J., Heng, P.-A.: Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 447\u2013456. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_50"},{"key":"52_CR5","doi-asserted-by":"crossref","unstructured":"Cui, S., Mao, L., Jiang, J., Liu, C., Xiong, S.: Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. J. Healthcare Eng. 2018 (2018). Article ID 4940593","DOI":"10.1155\/2018\/4940593"},{"key":"52_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"key":"52_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/978-3-319-46723-8_54","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"M Havaei","year":"2016","unstructured":"Havaei, M., Guizard, N., Chapados, N., Bengio, Y.: HeMIS: hetero-modal image segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 469\u2013477. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_54"},{"key":"52_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/978-3-319-75238-9_25","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"F Isensee","year":"2018","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287\u2013297. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_25"},{"key":"52_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., et al.: Efficient multi-scale 3D cnn with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61\u201378 (2017)","journal-title":"Med. Image Anal."},{"issue":"7","key":"52_CR10","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1109\/TIP.2017.2685345","volume":"26","author":"J Lapuyade-Lahorgue","year":"2017","unstructured":"Lapuyade-Lahorgue, J., Xue, J.H., Ruan, S.: Segmenting multi-source images using hidden Markov fields with copula-based multivariate statistical distributions. IEEE Trans. Image Process. 26(7), 3187\u20133195 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"52_CR11","unstructured":"Lau, K., Adler, J., Sj\u00f6lund, J.: A unified representation network for segmentation with missing modalities. arXiv preprint arXiv:1908.06683 (2019)"},{"key":"52_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"52_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/978-3-030-20351-1_32","volume-title":"Information Processing in Medical Imaging","author":"Y Shen","year":"2019","unstructured":"Shen, Y., Gao, M.: Brain tumor segmentation on MRI with missing modalities. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 417\u2013428. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_32"},{"key":"52_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/978-3-319-75238-9_16","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"G Wang","year":"2018","unstructured":"Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178\u2013190. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_16"},{"key":"52_CR15","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2017.10.002","volume":"43","author":"X Zhao","year":"2018","unstructured":"Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating fcnns and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98\u2013111 (2018)","journal-title":"Med. Image Anal."},{"key":"52_CR16","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","volume":"3","author":"T Zhou","year":"2019","unstructured":"Zhou, T., Ruan, S., Canu, S.: A review: deep learning for medical image segmentation using multi-modality fusion. Array 3, 100004 (2019)","journal-title":"Array"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59719-1_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T14:57:18Z","timestamp":1619276238000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59719-1_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597184","9783030597191"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59719-1_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1809","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":"542","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":"30% - 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":"4","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}