{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:37:16Z","timestamp":1778081836467,"version":"3.51.4"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030937218","type":"print"},{"value":"9783030937225","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-93722-5_34","type":"book-chapter","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T15:04:40Z","timestamp":1642172680000},"page":"315-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Refined Deep Layer Aggregation for\u00a0Multi-Disease, Multi-View & Multi-Center Cardiac MR Segmentation"],"prefix":"10.1007","author":[{"given":"Di","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhennan","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leon","family":"Axel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris N.","family":"Metaxas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"issue":"11","key":"34_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":"34_CR2","doi-asserted-by":"crossref","unstructured":"Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge. IEEE Trans. Med. Imaging 40, 3543\u20133554 (2021)","DOI":"10.1109\/TMI.2021.3090082"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Chang, Q., Yan, Z., Lou, Y., Axel, L., Metaxas, D.N.: Soft-label guided semi-supervised learning for bi-ventricle segmentation in cardiac cine MRI. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1752\u20131755. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098546"},{"key":"34_CR4","unstructured":"Han, X., Xu, C., Prince, J.L.: A topology preserving deformable model using level sets. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 2, pp. 2. IEEE (2001)"},{"key":"34_CR5","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"},{"issue":"15","key":"34_CR6","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1016\/j.jacc.2009.04.094","volume":"54","author":"TD Karamitsos","year":"2009","unstructured":"Karamitsos, T.D., Francis, J.M., Myerson, S., Selvanayagam, J.B., Neubauer, S.: The role of cardiovascular magnetic resonance imaging in heart failure. J. Am. Coll. Cardiol. 54(15), 1407\u20131424 (2009)","journal-title":"J. Am. Coll. Cardiol."},{"key":"34_CR7","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","volume":"51","author":"M Khened","year":"2019","unstructured":"Khened, M., Kollerathu, V.A., Krishnamurthi, G.: Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med. Image Anal. 51, 21\u201345 (2019)","journal-title":"Med. Image Anal."},{"key":"34_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/978-3-319-46726-9_31","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016","author":"B Kong","year":"2016","unstructured":"Kong, B., Zhan, Y., Shin, M., Denny, T., Zhang, S.: Recognizing end-diastole and end-systole frames via deep temporal regression network. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 264\u2013272. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46726-9_31"},{"key":"34_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/978-3-030-12029-0_41","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"J Li","year":"2019","unstructured":"Li, J., Hu, Z.: Left ventricle full quantification using deep layer aggregation based multitask relationship learning. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 381\u2013388. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_41"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Fully automatic segmentation of short-axis cardiac MRI using modified deep layer aggregation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 793\u2013797. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759516"},{"key":"34_CR11","unstructured":"Metaxas, D.N.: Physics-based Deformable Models: Applications to Computer Vision, Graphics and Medical Imaging, vol. 389. Springer Science & Business Media, Boston (2012)"},{"key":"34_CR12","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.media.2016.05.009","volume":"35","author":"TA Ngo","year":"2017","unstructured":"Ngo, T.A., Lu, Z., Carneiro, G.: Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med. Image Anal. 35, 159\u2013171 (2017)","journal-title":"Med. Image Anal."},{"issue":"2","key":"34_CR13","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s10334-015-0521-4","volume":"29","author":"P Peng","year":"2016","unstructured":"Peng, P., Lekadir, K., Gooya, A., Shao, L., Petersen, S.E., Frangi, A.F.: A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Mag. Reson. Mater. Phys. Biol. Med. 29(2), 155\u2013195 (2016). https:\/\/doi.org\/10.1007\/s10334-015-0521-4","journal-title":"Mag. Reson. Mater. Phys. Biol. Med."},{"issue":"2","key":"34_CR14","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.media.2010.12.004","volume":"15","author":"C Petitjean","year":"2011","unstructured":"Petitjean, C., Dacher, J.N.: A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 15(2), 169\u2013184 (2011)","journal-title":"Med. Image Anal."},{"key":"34_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-319-52280-7_8","volume-title":"Reconstruction, Segmentation, and Analysis of Medical Images","author":"RPK Poudel","year":"2017","unstructured":"Poudel, R.P.K., Lamata, P., Montana, G.: Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. In: Zuluaga, M.A., Bhatia, K., Kainz, B., Moghari, M.H., Pace, D.F. (eds.) RAMBO\/HVSMR -2016. LNCS, vol. 10129, pp. 83\u201394. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-52280-7_8"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Queir\u00f3s, S., et al.: Fast automatic myocardial segmentation in 4d cine CMR datasets. Med. Image Anal. 18(7), 1115\u20131131 (2014)","DOI":"10.1016\/j.media.2014.06.001"},{"key":"34_CR17","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.media.2017.04.002","volume":"39","author":"LK Tan","year":"2017","unstructured":"Tan, L.K., Liew, Y.M., Lim, E., McLaughlin, R.A.: Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences. Med. Image Anal. 39, 78\u201386 (2017)","journal-title":"Med. Image Anal."},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Learning a discriminative feature network for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1857\u20131866 (2018)","DOI":"10.1109\/CVPR.2018.00199"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403\u20132412 (2018)","DOI":"10.1109\/CVPR.2018.00255"},{"key":"34_CR20","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.media.2016.02.006","volume":"31","author":"X Zhuang","year":"2016","unstructured":"Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77\u201387 (2016)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93722-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T14:38:27Z","timestamp":1651156707000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93722-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030937218","9783030937225"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93722-5_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 January 2022","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stacom2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/stacom2021.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":"48","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":"40","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":"83% - 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 accepted papers split in 25 regular papers and 15 Challenge papers. The workshop took place 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)"}}]}}