{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:09:22Z","timestamp":1742965762901,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030937218"},{"type":"electronic","value":"9783030937225"}],"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_35","type":"book-chapter","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T15:04:40Z","timestamp":1642172680000},"page":"323-334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multi-view Crossover Attention U-Net Cascade with\u00a0Fourier Domain Adaptation for\u00a0Multi-domain Cardiac MRI Segmentation"],"prefix":"10.1007","author":[{"given":"Marcel","family":"Beetz","sequence":"first","affiliation":[]},{"given":"Jorge","family":"Corral Acero","sequence":"additional","affiliation":[]},{"given":"Vicente","family":"Grau","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"issue":"3","key":"35_CR1","first-page":"323","volume":"17","author":"K Alfakih","year":"2003","unstructured":"Alfakih, K., Plein, S., Thiele, H., Jones, T., Ridgway, J.P., Sivananthan, M.U.: Normal human left and right ventricular dimensions for MRI as assessed by turbo gradient echo and steady-state free precession imaging sequences. J. Magn. Reson. Imaging Official J. Int. Soc. Magn. Reson. Med. 17(3), 323\u2013329 (2003)","journal-title":"J. Magn. Reson. Imaging Official J. Int. Soc. Magn. Reson. Med."},{"key":"35_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/978-3-030-59719-1_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"B Audelan","year":"2020","unstructured":"Audelan, B., Hamzaoui, D., Montagne, S., Renard-Penna, R., Delingette, H.: Robust fusion of probability maps. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 259\u2013268. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_26"},{"issue":"1","key":"35_CR3","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.media.2015.08.009","volume":"26","author":"W Bai","year":"2015","unstructured":"Bai, W., et al.: A bi-ventricular cardiac atlas built from 1000+ high resolution mr images of healthy subjects and an analysis of shape and motion. Med. Image Anal. 26(1), 133\u2013145 (2015)","journal-title":"Med. Image Anal."},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Beetz, M., Banerjee, A., Grau, V.: Biventricular surface reconstruction from cine MRI contours using point completion networks. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 105\u2013109 (2021)","DOI":"10.1109\/ISBI48211.2021.9434040"},{"key":"35_CR5","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1109\/TMI.2021.3090082","volume":"40","author":"VM Campello","year":"2021","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)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"35_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/978-3-030-32245-8_58","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"C Chen","year":"2019","unstructured":"Chen, C., Biffi, C., Tarroni, G., Petersen, S., Bai, W., Rueckert, D.: Learning shape priors for robust cardiac mr segmentation from multi-view images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 523\u2013531. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_58"},{"key":"35_CR7","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fcvm.2020.00025","volume":"7","author":"C Chen","year":"2020","unstructured":"Chen, C., et al.: Deep learning for cardiac image segmentation: a review. Front. Cardiovasc. Med. 7, 25 (2020)","journal-title":"Front. Cardiovasc. Med."},{"issue":"48","key":"35_CR8","doi-asserted-by":"publisher","first-page":"4556","DOI":"10.1093\/eurheartj\/ehaa159","volume":"41","author":"J Corral-Acero","year":"2020","unstructured":"Corral-Acero, J., et al.: The digital twin to enable the vision of precision cardiology. Eur. Heart J. 41(48), 4556\u20134564 (2020)","journal-title":"Eur. Heart J."},{"key":"35_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/978-3-030-68107-4_20","volume-title":"Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges","author":"J Corral Acero","year":"2021","unstructured":"Corral Acero, J., Sundaresan, V., Dinsdale, N., Grau, V., Jenkinson, M.: A 2-step deep learning method with domain adaptation for multi-centre, multi-vendor and multi-disease cardiac magnetic resonance segmentation. In: Puyol Anton, E., et al. (eds.) STACOM 2020. LNCS, vol. 12592, pp. 196\u2013207. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68107-4_20"},{"key":"35_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/978-3-030-39074-7_40","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges","author":"J Corral Acero","year":"2020","unstructured":"Corral Acero, J., et al.: Left ventricle quantification with cardiac MRI: deep learning meets statistical models of deformation. In: Pop, M., et al. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 384\u2013394. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-39074-7_40"},{"key":"35_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-030-21949-9_39","volume-title":"Functional Imaging and Modeling of the Heart","author":"J Corral Acero","year":"2019","unstructured":"Corral Acero, J., et al.: SMOD - data augmentation based on statistical models of deformation to enhance segmentation in 2d cine cardiac MRI. In: Coudi\u00e8re, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds.) FIMH 2019. LNCS, vol. 11504, pp. 361\u2013369. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21949-9_39"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Dall\u2019Armellina, E.: From recognized to novel quantitative CMR biomarkers of lv recovery: a paradigm shift in acute myocardial infarction imaging (2017)","DOI":"10.1016\/j.jcmg.2016.07.007"},{"issue":"11","key":"35_CR13","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1016\/j.jacc.2018.12.054","volume":"73","author":"D Dey","year":"2019","unstructured":"Dey, D., et al.: Artificial intelligence in cardiovascular imaging: jacc state-of-the-art review. J. Am. Coll. Cardiol. 73(11), 1317\u20131335 (2019)","journal-title":"J. Am. Coll. Cardiol."},{"key":"35_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"35_CR15","unstructured":"Martin-Isla, C.: Multi-disease, multi-view & multi-center right ventricular segmentation in cardiac MRI (M&Ms-2) (2021). https:\/\/www.ub.edu\/mnms-2\/"},{"key":"35_CR16","unstructured":"Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"35_CR17","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8026\u20138037 (2019)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"35_CR18","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s12968-017-0327-9","volume":"19","author":"SE Petersen","year":"2017","unstructured":"Petersen, S.E., et al.: Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (cmr) in caucasians from the uk biobank population cohort. J. Cardiovasc. Mag. Reson. 19(1), 18 (2017)","journal-title":"J. Cardiovasc. Mag. Reson."},{"issue":"14","key":"35_CR19","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1136\/heartjnl-2017-311198","volume":"104","author":"K Shameer","year":"2018","unstructured":"Shameer, K., Johnson, K.W., Glicksberg, B.S., Dudley, J.T., Sengupta, P.P.: Machine learning in cardiovascular medicine: are we there yet? Heart 104(14), 1156\u20131164 (2018)","journal-title":"Heart"},{"issue":"4","key":"35_CR20","doi-asserted-by":"publisher","first-page":"151","DOI":"10.18773\/austprescr.2017.045","volume":"40","author":"MB Stokes","year":"2017","unstructured":"Stokes, M.B., Roberts-Thomson, R.: The role of cardiac imaging in clinical practice. Aust. Prescriber 40(4), 151 (2017)","journal-title":"Aust. Prescriber"},{"issue":"1","key":"35_CR21","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1148\/radiol.2018180513","volume":"290","author":"Q Tao","year":"2019","unstructured":"Tao, Q., et al.: Deep learning-based method for fully automatic quantification of left ventricle function from cine mr images: a multivendor, multicenter study. Radiology 290(1), 81\u201388 (2019)","journal-title":"Radiology"},{"key":"35_CR22","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":"35_CR23","doi-asserted-by":"crossref","unstructured":"Yang, Y., Soatto, S.: FDA: fourier domain adaptation for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4085\u20134095 (2020)","DOI":"10.1109\/CVPR42600.2020.00414"}],"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_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T14:38:18Z","timestamp":1651156698000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93722-5_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030937218","9783030937225"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93722-5_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}