{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:56:35Z","timestamp":1768269395665,"version":"3.49.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322441","type":"print"},{"value":"9783030322458","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32245-8_68","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"614-622","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["MSU-Net: Multiscale Statistical U-Net for Real-Time 3D Cardiac MRI Video Segmentation"],"prefix":"10.1007","author":[{"given":"Tianchen","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jinjun","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Xiaowei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Haiyun","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Meiping","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Zhuang","sequence":"additional","affiliation":[]},{"given":"Yiyu","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"11","key":"68_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"},{"issue":"4","key":"68_CR2","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1002\/jmri.25749","volume":"46","author":"AE Campbell-Washburn","year":"2017","unstructured":"Campbell-Washburn, A.E., et al.: Real-time MRI guidance of cardiac interventions. J. Magn. Reson. Imaging 46(4), 935\u2013950 (2017)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"3","key":"68_CR3","first-page":"374","volume":"5","author":"PW Iltis","year":"2015","unstructured":"Iltis, P.W., Frahm, J., Voit, D., Joseph, A.A., Schoonderwaldt, E., Altenm\u00fcller, E.: High-speed real-time magnetic resonance imaging of fast tongue movements in elite horn players. Quant. Imaging Med. Surg. 5(3), 374 (2015)","journal-title":"Quant. Imaging Med. Surg."},{"key":"68_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-319-75541-0_13","volume-title":"Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges","author":"F Isensee","year":"2018","unstructured":"Isensee, F., Jaeger, P.F., Full, P.M., Wolf, I., Engelhardt, S., Maier-Hein, K.H.: Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 120\u2013129. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75541-0_13"},{"key":"68_CR5","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116\u2013131 (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"issue":"5","key":"68_CR6","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1002\/mrm.21044","volume":"56","author":"ER McVeigh","year":"2006","unstructured":"McVeigh, E.R., et al.: Real-time interactive MRI-guided cardiac surgery: aortic valve replacement using a direct apical approach. Magn. Reson. Med.: Official J. Int. Soc. Magn. Reson. Med. 56(5), 958\u2013964 (2006)","journal-title":"Magn. Reson. Med.: Official J. Int. Soc. Magn. Reson. Med."},{"key":"68_CR7","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":"68_CR8","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"68_CR9","doi-asserted-by":"crossref","unstructured":"Schaetz, S., Voit, D., Frahm, J., Uecker, M.: Accelerated computing in magnetic resonance imaging: real-time imaging using nonlinear inverse reconstruction. In: Computational and Mathematical Methods in Medicine 2017 (2017)","DOI":"10.1155\/2017\/3527269"},{"key":"68_CR10","doi-asserted-by":"crossref","unstructured":"Wang, T., Xiong, J., Xu, X., Shi, Y.: SCNN: a general distribution based statistical convolutional neural network with application to video object detection. arXiv preprint arXiv:1903.07663 (2019)","DOI":"10.1609\/aaai.v33i01.33015321"},{"key":"68_CR11","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.1109\/TMI.2018.2820742","volume":"37","author":"Q Zheng","year":"2018","unstructured":"Zheng, Q., Delingette, H., Duchateau, N., Ayache, N.: 3D consistent and robust segmentation of cardiac images by deep learning with spatial propagation. IEEE Trans. Med. Imaging 37, 2137\u20132148 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"68_CR12","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1109\/JBHI.2018.2865450","volume":"23","author":"C Zotti","year":"2018","unstructured":"Zotti, C., Luo, Z., Lalande, A., Jodoin, P.M.: Convolutional neural network with shape prior applied to cardiac MRI segmentation. IEEE J. Biomed. Health Inf. 23, 1119\u20131128 (2018)","journal-title":"IEEE J. Biomed. Health Inf."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32245-8_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:03:06Z","timestamp":1728518586000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32245-8_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322441","9783030322458"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32245-8_68","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","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":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}