{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:02:03Z","timestamp":1760598123490,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"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_9","type":"book-chapter","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T15:04:40Z","timestamp":1642172680000},"page":"75-83","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders"],"prefix":"10.1007","author":[{"given":"Marcel","family":"Beetz","sequence":"first","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":"9_CR1","doi-asserted-by":"crossref","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)","DOI":"10.1016\/j.media.2015.08.009"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Banerjee, A., et al.: A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices. Philosoph. Trans. Royal Soc. A., p. 20200257 (2021)","DOI":"10.1098\/rsta.2020.0257"},{"key":"9_CR3","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":"9_CR4","doi-asserted-by":"crossref","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)","DOI":"10.1109\/2945.817351"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Biffi, C., et al.: Explainable anatomical shape analysis through deep hierarchical generative models. IEEE Trans. Med. Imaging 39(6), 2088\u20132099 (2020)","DOI":"10.1109\/TMI.2020.2964499"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Gilbert, K., Mauger, C., Young, A.A., Suinesiaputra, A.: Artificial intelligence in cardiac imaging with statistical atlases of cardiac anatomy. Front. Cardiovasc. Med. 7, 102 (2020)","DOI":"10.3389\/fcvm.2020.00102"},{"key":"9_CR7","unstructured":"Higgins, I., et al.: beta-VAE: learning basic visual concepts with a constrained variational framework. In: 5th International Conference on Learning Representations (ICLR), pp. 1\u201313 (2017)"},{"key":"9_CR8","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","DOI":"10.1016\/j.media.2017.07.005"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Petersen, S.E., et al.: UK Biobank\u2019s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18(1), 1\u20137 (2015)","DOI":"10.1186\/s12968-016-0227-4"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Piazzese, C., Carminati, M.C., Pepi, M., Caiani, E.G.: Statistical shape models of the heart: applications to cardiac imaging. In: Statistical Shape and Deformation Analysis, pp. 445\u2013480. Elsevier (2017)","DOI":"10.1016\/B978-0-12-810493-4.00019-5"},{"key":"9_CR12","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":"9_CR13","doi-asserted-by":"crossref","unstructured":"Rezaei, M.: Chapter 5 - Generative adversarial network for cardiovascular imaging. In: Al\u2019Aref, S.J., Singh, G., Baskaran, L., Metaxas, D. (eds.) Machine Learning in Cardiovascular Medicine, pp. 95\u2013121. Academic Press (2021)","DOI":"10.1016\/B978-0-12-820273-9.00005-1"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Tavakoli, V., Amini, A.A.: A survey of shaped-based registration and segmentation techniques for cardiac images. Comput. Vision Image Understanding, 117(9), 966\u2013989 (2013)","DOI":"10.1016\/j.cviu.2012.11.017"},{"key":"9_CR15","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_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T14:33:18Z","timestamp":1651156398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93722-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030937218","9783030937225"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93722-5_9","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)"}}]}}