{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T12:40:51Z","timestamp":1759495251058,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"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_24","type":"book-chapter","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T15:04:40Z","timestamp":1642172680000},"page":"219-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Predicting 3D Cardiac Deformations with\u00a0Point Cloud Autoencoders"],"prefix":"10.1007","author":[{"given":"Marcel","family":"Beetz","sequence":"first","affiliation":[]},{"given":"Julius","family":"Ossenberg-Engels","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8198-5128","authenticated-orcid":false,"given":"Abhirup","family":"Banerjee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8139-3480","authenticated-orcid":false,"given":"Vicente","family":"Grau","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"24_CR1","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"},{"issue":"2","key":"24_CR2","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/s42256-019-0019-2","volume":"1","author":"GA Bello","year":"2019","unstructured":"Bello, G.A., et al.: Deep-learning cardiac motion analysis for human survival prediction. Nat. Mach. Intell. 1(2), 95\u2013104 (2019)","journal-title":"Nat. Mach. Intell."},{"issue":"4","key":"24_CR3","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1109\/2945.817351","volume":"5","author":"F Bernardini","year":"1999","unstructured":"Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., Taubin, G.: The ball-pivoting algorithm for surface reconstruction. IEEE Trans. Visual Comput. Graphics 5(4), 349\u2013359 (1999)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"24_CR4","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.neucom.2020.08.030","volume":"418","author":"Y Chang","year":"2020","unstructured":"Chang, Y., Jung, C.: Automatic cardiac MRI segmentation and permutation-invariant pathology classification using deep neural networks and point clouds. Neurocomputing 418, 270\u2013279 (2020)","journal-title":"Neurocomputing"},{"key":"24_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/978-3-030-39074-7_19","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges","author":"J Krebs","year":"2020","unstructured":"Krebs, J., Mansi, T., Ayache, N., Delingette, H.: Probabilistic motion modeling from\u00a0medical image sequences: application to cardiac cine-MRI. In: Pop, M., et al. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 176\u2013185. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-39074-7_19"},{"key":"24_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-030-00889-5_12","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"J Krebs","year":"2018","unstructured":"Krebs, J., Mansi, T., Mailh\u00e9, B., Ayache, N., Delingette, H.: Unsupervised probabilistic deformation modeling for robust diffeomorphic registration. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 101\u2013109. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_12"},{"key":"24_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-030-39074-7_12","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges","author":"J Ossenberg-Engels","year":"2020","unstructured":"Ossenberg-Engels, J., Grau, V.: Conditional generative adversarial networks for the prediction of cardiac contraction from individual frames. In: Pop, M., et al. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 109\u2013118. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-39074-7_12"},{"issue":"1","key":"24_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12968-016-0227-4","volume":"18","author":"SE Petersen","year":"2015","unstructured":"Petersen, S.E., et al.: UK Biobank\u2019s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18(1), 1\u20137 (2015)","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"24_CR9","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5099\u20135108 (2017)"},{"key":"24_CR10","unstructured":"WHO: Cardiovascular disease death rate (2019). https:\/\/www.who.int\/en\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)"},{"key":"24_CR11","unstructured":"Yang, Y., Feng, C., Shen, Y., Tian, D.: FoldingNet: interpretable unsupervised learning on 3D point clouds. arXiv preprint arXiv:1712.07262 (2017)"},{"key":"24_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-030-68107-4_12","volume-title":"Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges","author":"M Ye","year":"2021","unstructured":"Ye, M., et al.: PC-U net: learning to jointly reconstruct and segment the cardiac walls in 3D from CT data. In: Puyol Anton, E., et al. (eds.) STACOM 2020. LNCS, vol. 12592, pp. 117\u2013126. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68107-4_12"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: PCN: point completion network. In: 2018 International Conference on 3D Vision (3DV), pp. 728\u2013737 (2018)","DOI":"10.1109\/3DV.2018.00088"}],"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_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T23:52:26Z","timestamp":1674431546000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93722-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030937218","9783030937225"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93722-5_24","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)"}}]}}