{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T01:02:44Z","timestamp":1766278964749,"version":"3.48.0"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030656508"},{"type":"electronic","value":"9783030656515"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-65651-5_16","type":"book-chapter","created":{"date-parts":[[2020,12,20]],"date-time":"2020-12-20T19:02:47Z","timestamp":1608490967000},"page":"167-176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Recognition and Standardization of Cardiac MRI Orientation via Multi-tasking Learning and Deep Neural Networks"],"prefix":"10.1007","author":[{"given":"Ke","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xiahai","family":"Zhuang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,12,21]]},"reference":[{"issue":"11","key":"16_CR1","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. imaging"},{"key":"16_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/978-3-030-59716-0_23","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"W Ding","year":"2020","unstructured":"Ding, W., Li, L., Zhuang, X., Huang, L.: Cross-modality multi-atlas segmentation using deep neural networks. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part III. LNCS, vol. 12263, pp. 233\u2013242. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_23"},{"issue":"9","key":"16_CR3","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1109\/TMI.2019.2894322","volume":"38","author":"J Duan","year":"2019","unstructured":"Duan, J., et al.: Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach. IEEE Trans. Med. Imaging 38(9), 2151\u20132164 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR4","unstructured":"He, T., Guo, J., Wang, J., Xu, X., Yi, Z.: Multi-task learning for the segmentation of thoracic organs at risk in CT images. In: SegTHOR@ISBI (2019)"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Li, L., Weng, X., Schnabel, J.A., Zhuang, X.: Joint left atrial segmentation and scar quantification based on a DNN with spatial encoding and shape attention. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2020)","DOI":"10.1007\/978-3-030-59719-1_12"},{"key":"16_CR6","unstructured":"Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: AtrialJSQnet: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. arXiv preprint arXiv:2008.04729 (2020)"},{"key":"16_CR7","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.media.2018.05.008","volume":"48","author":"DM Vigneault","year":"2018","unstructured":"Vigneault, D.M., Xie, W., Ho, C.Y., Bluemke, D.A., Noble, J.A.: $$\\omega $$-net (omega-net): fully automatic, multi-view cardiac mr detection, orientation, and segmentation with deep neural networks. Med. Image Anal. 48, 95\u2013106 (2018)","journal-title":"Med. Image Anal."},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.media.2018.10.005","volume":"51","author":"JM Wolterink","year":"2019","unstructured":"Wolterink, J.M., van Hamersvelt, R.W., Viergever, M.A., Leiner, T., I\u0161gum, I.: Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med. Image Anal. 51, 46\u201360 (2019)","journal-title":"Med. Image Anal."},{"key":"16_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1007\/978-3-319-66179-7_32","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"W Xue","year":"2017","unstructured":"Xue, W., Lum, A., Mercado, A., Landis, M., Warrington, J., Li, S.: Full quantification of left ventricle via deep multitask learning network respecting intra- and inter-task relatedness. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 276\u2013284. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_32"},{"key":"16_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/978-3-030-32245-8_62","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Q Yue","year":"2019","unstructured":"Yue, Q., Luo, X., Ye, Q., Xu, L., Zhuang, X.: Cardiac segmentation from LGE MRI using deep neural network incorporating shape and spatial priors. In: Shen, D., et al. (eds.) MICCAI 2019, Part II. LNCS, vol. 11765, pp. 559\u2013567. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_62"},{"key":"16_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/978-3-319-46723-8_67","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"X Zhuang","year":"2016","unstructured":"Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016, Part II. LNCS, vol. 9901, pp. 581\u2013588. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_67"},{"issue":"12","key":"16_CR12","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","volume":"41","author":"X Zhuang","year":"2019","unstructured":"Zhuang, X., et al.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2933\u20132946 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Zhuang, X., et al.: Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med. Image Anal. 58, 101537 (2019)","DOI":"10.1016\/j.media.2019.101537"}],"container-title":["Lecture Notes in Computer Science","Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65651-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T01:02:04Z","timestamp":1766278924000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-65651-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030656508","9783030656515"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65651-5_16","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":"21 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MyoPS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge","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":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"myops2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.sdspeople.fudan.edu.cn\/zhuangxiahai\/0\/myops20\/","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":"17","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":"12","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":"4","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":"71% - 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":"2.5","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The challenge 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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}