{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T20:46:46Z","timestamp":1759178806990,"version":"3.41.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031945618","type":"print"},{"value":"9783031945625","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-94562-5_34","type":"book-chapter","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T10:06:39Z","timestamp":1750413999000},"page":"372-383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Open-Source End-to-End Pipeline for\u00a0Generating 3D+t Biventricular Meshes from\u00a0Cardiac Magnetic Resonance Imaging"],"prefix":"10.1007","author":[{"given":"Joshua R.","family":"Dillon","sequence":"first","affiliation":[]},{"given":"Charl\u00e8ne","family":"Mauger","sequence":"additional","affiliation":[]},{"given":"Debbie","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Steffen E.","family":"Petersen","sequence":"additional","affiliation":[]},{"given":"Andrew D.","family":"McCulloch","sequence":"additional","affiliation":[]},{"given":"Alistair A.","family":"Young","sequence":"additional","affiliation":[]},{"given":"Martyn P.","family":"Nash","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","unstructured":"Banerjee, A., et al.: A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices. Philos. Trans. Roy. Soc. A: Math. Phys. Eng. Sci. 379(2212), 20200257 (2021). https:\/\/doi.org\/10.1098\/rsta.2020.0257","DOI":"10.1098\/rsta.2020.0257"},{"key":"34_CR2","doi-asserted-by":"publisher","unstructured":"Beetz, M., Banerjee, A., Ossenberg-Engels, J., Grau, V.: Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images. Med. Image Anal. 90, 102975 (2023). https:\/\/doi.org\/10.1016\/j.media.2023.102975","DOI":"10.1016\/j.media.2023.102975"},{"key":"34_CR3","doi-asserted-by":"publisher","unstructured":"Deng, Y., et al.: ModusGraph: automated 3D and 4D mesh model reconstruction from cine CMR with improved accuracy and efficiency. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14226, pp. 173\u2013183. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43990-2_17","DOI":"10.1007\/978-3-031-43990-2_17"},{"issue":"1","key":"34_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s12968-023-00924-1","volume":"25","author":"S Govil","year":"2023","unstructured":"Govil, S., et al.: A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot. J. Cardiovasc. Magn. Reson. 25(1), 15 (2023). https:\/\/doi.org\/10.1186\/s12968-023-00924-1","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"34_CR5","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"34_CR6","doi-asserted-by":"publisher","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S., Petersen, J., Maier-Hein, K.H.: nnU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","DOI":"10.1038\/s41592-020-01008-z"},{"key":"34_CR7","doi-asserted-by":"publisher","unstructured":"Kawaji, K., et al.: Automated segmentation of routine clinical cardiac magnetic resonance imaging for assessment of left ventricular diastolic dysfunction. Circ.: Cardiovasc. Imaging 2(6), 476\u2013484 (2009). https:\/\/doi.org\/10.1161\/CIRCIMAGING.109.879304","DOI":"10.1161\/CIRCIMAGING.109.879304"},{"key":"34_CR8","doi-asserted-by":"publisher","unstructured":"Mart\u00edn-Isla, C., et al.: Deep learning segmentation of the right ventricle in cardiac MRI: the M &Ms challenge. IEEE J. Biomed. Health Inform. 27(7), 3302\u20133313 (2023). https:\/\/doi.org\/10.1109\/JBHI.2023.3267857","DOI":"10.1109\/JBHI.2023.3267857"},{"key":"34_CR9","doi-asserted-by":"publisher","unstructured":"Mauger, C., et al.: An iterative diffeomorphic algorithm for registration of subdivision surfaces: application to congenital heart disease. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2018, pp. 596\u2013599 (2018). https:\/\/doi.org\/10.1109\/EMBC.2018.8512394","DOI":"10.1109\/EMBC.2018.8512394"},{"issue":"1","key":"34_CR10","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/1532-429X-12-46","volume":"12","author":"DD Mendoza","year":"2010","unstructured":"Mendoza, D.D., et al.: Impact of diastolic dysfunction severity on global left ventricular volumetric filling - assessment by automated segmentation of routine cine cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 12(1), 46 (2010). https:\/\/doi.org\/10.1186\/1532-429X-12-46","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"34_CR11","doi-asserted-by":"publisher","unstructured":"Miller, R., Kerfoot, E., Mauger, C., Ismail, T.F., Young, A.A., Nordsletten, D.A.: An implementation of patient-specific biventricular mechanics simulations with a deep learning and computational pipeline. Front. Physiol. 12 (2021). https:\/\/doi.org\/10.3389\/fphys.2021.716597","DOI":"10.3389\/fphys.2021.716597"},{"issue":"1","key":"34_CR12","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s12968-016-0227-4","volume":"18","author":"SE Petersen","year":"2016","unstructured":"Petersen, S.E., et al.: UK Biobank\u2019s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18(1), 8 (2016). https:\/\/doi.org\/10.1186\/s12968-016-0227-4","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"34_CR13","doi-asserted-by":"publisher","unstructured":"Schuler, S., Pilia, N., Potyagaylo, D., Loewe, A.: Cobiveco: consistent biventricular coordinates for precise and intuitive description of position in the heart - with MATLAB implementation. Med. Image Anal. 74, 102247 (2021). https:\/\/doi.org\/10.1016\/j.media.2021.102247","DOI":"10.1016\/j.media.2021.102247"},{"key":"34_CR14","doi-asserted-by":"publisher","unstructured":"Xia, Y., et al.: Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale. Med. Image Anal. 80, 102498 (2022). https:\/\/doi.org\/10.1016\/j.media.2022.102498","DOI":"10.1016\/j.media.2022.102498"},{"key":"34_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1007\/978-3-030-21949-9_37","volume-title":"Functional Imaging and Modeling of the Heart","author":"H Xu","year":"2019","unstructured":"Xu, H., Zacur, E., Schneider, J.E., Grau, V.: Ventricle surface reconstruction from cardiac MR slices using deep learning. In: Coudi\u00e8re, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds.) FIMH 2019. LNCS, vol. 11504, pp. 342\u2013351. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21949-9_37"},{"key":"34_CR16","doi-asserted-by":"publisher","unstructured":"Xu, Y., et al.: Improved 3D whole heart geometry from sparse CMR slices (2024). https:\/\/doi.org\/10.48550\/arXiv.2408.07532","DOI":"10.48550\/arXiv.2408.07532"},{"key":"34_CR17","doi-asserted-by":"publisher","unstructured":"Zhao, D., et al.: MITEA: a dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging. Front. Cardiovasc. Med. 9 (2023). https:\/\/doi.org\/10.3389\/fcvm.2022.1016703","DOI":"10.3389\/fcvm.2022.1016703"}],"container-title":["Lecture Notes in Computer Science","Functional Imaging and Modeling of the Heart"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-94562-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T10:06:39Z","timestamp":1750413999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94562-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031945618","9783031945625"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94562-5_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"29 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"The pipeline code, models, and documentation are available at .","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}},{"value":"FIMH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Functional Imaging and Modeling of the Heart","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dallas, TX","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fimh2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/fimh2025.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}