{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T14:51:17Z","timestamp":1769611877812,"version":"3.49.0"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030681067","type":"print"},{"value":"9783030681074","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-68107-4_2","type":"book-chapter","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T04:31:57Z","timestamp":1611808317000},"page":"14-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Multiplanar CT Reformatting from Trans-Axial into Left Ventricle Short-Axis View"],"prefix":"10.1007","author":[{"given":"Marta","family":"Nu\u00f1ez-Garcia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Cedilnik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuman","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maxime","family":"Sermesant","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hubert","family":"Cochet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"2_CR1","unstructured":"Simple ITK, SPIE2019 COURSE, 02 Images and resampling. https:\/\/simpleitk.org\/SPIE2019_COURSE\/02_images_and_resampling.html. Accessed 15 July 2020"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Alansary, A., Le Folgoc, L., et al.: Automatic view planning with multi-scale deep reinforcement learning agents. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 277\u2013285 (2018)","DOI":"10.1007\/978-3-030-00928-1_32"},{"issue":"6","key":"2_CR3","doi-asserted-by":"publisher","first-page":"e180069","DOI":"10.1148\/ryai.2019180069","volume":"1","author":"K Blansit","year":"2019","unstructured":"Blansit, K., Retson, T., et al.: Deep learning-based prescription of cardiac MRI Planes. Radiol. Artif. Intell. 1(6), e180069 (2019)","journal-title":"Radiol. Artif. Intell."},{"key":"2_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-030-21949-9_35","volume-title":"Functional Imaging and Modeling of the Heart","author":"N Cedilnik","year":"2019","unstructured":"Cedilnik, N., Duchateau, J., Sacher, F., Ja\u00efs, P., Cochet, H., Sermesant, M.: Fully automated electrophysiological model personalisation framework from CT imaging. In: Coudi\u00e8re, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds.) FIMH 2019. LNCS, vol. 11504, pp. 325\u2013333. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21949-9_35"},{"issue":"3","key":"2_CR5","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/0146-664X(80)90054-4","volume":"14","author":"PE Danielsson","year":"1980","unstructured":"Danielsson, P.E.: Euclidean distance mapping. Comput. Graph. Image Process. 14(3), 227\u2013248 (1980)","journal-title":"Comput. Graph. Image Process."},{"issue":"1","key":"2_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1322432.1322434","volume":"40","author":"R Fabbri","year":"2008","unstructured":"Fabbri, R., Costa, L.D.F., et al.: 2D Euclidean distance transform algorithms: a comparative survey. ACM Comput. Surv. (CSUR) 40(1), 1\u201344 (2008)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Jia, S., et al.: Automatically segmenting the left atrium from cardiac images using successive 3D U-nets and a contour loss. In: International Workshop on Statistical Atlases and Computational Models of the Heart, pp. 221\u2013229 (2018)","DOI":"10.1007\/978-3-030-12029-0_24"},{"key":"2_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-319-67558-9_13","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"M Le","year":"2017","unstructured":"Le, M., Lieman-Sifry, J., Lau, F., Sall, S., Hsiao, A., Golden, D.: Computationally efficient cardiac views projection using 3D convolutional neural networks. In: Cardoso, M., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 109\u2013116. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_13"},{"issue":"4","key":"2_CR9","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","volume":"21","author":"WE Lorensen","year":"1987","unstructured":"Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. ACM Siggraph Comput. Graph. 21(4), 163\u2013169 (1987)","journal-title":"ACM Siggraph Comput. Graph."},{"issue":"9","key":"2_CR10","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1016\/j.acra.2007.05.019","volume":"14","author":"MT Lu","year":"2007","unstructured":"Lu, M.T., Ersoy, H., Whitmore, A.G., Lipton, M.J., Rybicki, F.J.: Reformatted four-chamber and short-axis views of the heart using thin section ($$\\le $$ 2 mm) MDCT images. Acad. Radiol. 14(9), 1108\u20131112 (2007)","journal-title":"Acad. Radiol."},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Lu, X., Jolly, M.P., Georgescu, B., et al.: Automatic view planning for cardiac MRI acquisition. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 479\u2013486 (2011)","DOI":"10.1007\/978-3-642-23626-6_59"},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.ejro.2016.03.003","volume":"3","author":"S Marchesseau","year":"2016","unstructured":"Marchesseau, S., Ho, J.X., Totman, J.J.: Influence of the short-axis cine acquisition protocol on the cardiac function evaluation: a reproducibility study. Eur. J. Radiol. Open 3, 60\u201366 (2016)","journal-title":"Eur. J. Radiol. Open"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"2_CR14","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1109\/TVCG.2007.70430","volume":"14","author":"S Valette","year":"2008","unstructured":"Valette, S., Chassery, J.M., Prost, R.: Generic remeshing of 3D triangular meshes with metric-dependent discrete Voronoi diagrams. IEEE Trans. Visual. Comput. Graph. 14(2), 369\u2013381 (2008)","journal-title":"IEEE Trans. Visual. Comput. Graph."}],"container-title":["Lecture Notes in Computer Science","Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68107-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T01:01:59Z","timestamp":1769562119000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-68107-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030681067","9783030681074"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68107-4_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"29 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"STACOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Statistical Atlases and Computational Models of the Heart","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":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stacom2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/stacom2020.cardiacatlas.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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70","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":"43","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":"61% - 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","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","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}