{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:33:02Z","timestamp":1783438382687,"version":"3.54.6"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597122","type":"print"},{"value":"9783030597139","type":"electronic"}],"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.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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59713-9_60","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T05:06:21Z","timestamp":1601615181000},"page":"623-632","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Temporal-Consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape"],"prefix":"10.1007","author":[{"given":"Hongrong","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wufeng","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"60_CR1","doi-asserted-by":"crossref","unstructured":"Al-Kadi, O.S.: Spatio-temporal segmentation in 3D echocardiographic sequences using fractional Brownian motion. IEEE Trans. Biomed. Eng. (2019)","DOI":"10.1109\/TBME.2019.2958701"},{"issue":"1","key":"60_CR2","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26\u201341 (2008)","journal-title":"Med. Image Anal."},{"issue":"8","key":"60_CR3","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR4","unstructured":"Chen, S., Ma, K., Zheng, Y.: TAN: temporal affine network for real-time left ventricle anatomical structure analysis based on 2D ultrasound videos. arXiv preprint arXiv:1904.00631 (2019)"},{"key":"60_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2019.2900628","volume":"7","author":"X Du","year":"2019","unstructured":"Du, X., Yin, S., Tang, R., Zhang, Y., Li, S.: Cardiac-DeepIED: automatic pixel-level deep segmentation for cardiac bi-ventricle using improved end-to-end encoder-decoder network. IEEE J. Transl. Eng. Health Med. 7, 1\u201310 (2019)","journal-title":"IEEE J. Transl. Eng. Health Med."},{"issue":"4","key":"60_CR6","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1161\/01.CIR.60.4.760","volume":"60","author":"E Folland","year":"1979","unstructured":"Folland, E., Parisi, A., Moynihan, P., Jones, D.R., Feldman, C.L., Tow, D.: Assessment of left ventricular ejection fraction and volumes by real-time, two-dimensional echocardiography. A comparison of cineangiographic and radionuclide techniques. Circulation 60(4), 760\u2013766 (1979)","journal-title":"Circulation"},{"key":"60_CR7","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017\u20132025 (2015)"},{"key":"60_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/978-3-030-00889-5_4","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"MH Jafari","year":"2018","unstructured":"Jafari, M.H., et al.: A unified framework integrating recurrent fully-convolutional networks and optical flow for segmentation of the left ventricle in echocardiography data. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS 2018. LNCS, vol. 11045, pp. 29\u201337. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_4"},{"issue":"9","key":"60_CR9","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","volume":"38","author":"S Leclerc","year":"2019","unstructured":"Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging 38(9), 2198\u20132210 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1007\/978-3-030-32245-8_75","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Li","year":"2019","unstructured":"Li, M., et al.: Recurrent aggregation learning for multi-view echocardiographic sequences segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 678\u2013686. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_75"},{"issue":"2","key":"60_CR11","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2017","unstructured":"Oktay, O., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384\u2013395 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"60_CR12","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1109\/TMI.2017.2734959","volume":"36","author":"J Pedrosa","year":"2017","unstructured":"Pedrosa, J., et al.: Fast and fully automatic left ventricular segmentation and tracking in echocardiography using shape-based b-spline explicit active surfaces. IEEE Trans. Med. Imaging 36(11), 2287\u20132296 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-030-00934-2_53","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"C Qin","year":"2018","unstructured":"Qin, C., et al.: Joint learning of motion estimation and segmentation for cardiac MR image sequences. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 472\u2013480. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_53"},{"key":"60_CR14","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":"60_CR15","doi-asserted-by":"crossref","unstructured":"Savioli, N., Vieira, M.S., Lamata, P., Montana, G.: Automated segmentation on the entire cardiac cycle using a deep learning work-flow. In: 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 153\u2013158. IEEE (2018)","DOI":"10.1109\/SNAMS.2018.8554962"},{"key":"60_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-3-030-00937-3_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"W Yan","year":"2018","unstructured":"Yan, W., Wang, Y., Li, Z., van der Geest, R.J., Tao, Q.: Left ventricle segmentation via optical-flow-net from short-axis cine MRI: preserving the temporal coherence of cardiac motion. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 613\u2013621. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_70"}],"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\/978-3-030-59713-9_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:09:49Z","timestamp":1759356589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59713-9_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597122","9783030597139"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59713-9_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}