{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:06:26Z","timestamp":1742958386586,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031353017"},{"type":"electronic","value":"9783031353024"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-35302-4_22","type":"book-chapter","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T07:02:40Z","timestamp":1686812560000},"page":"213-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning-Based Emulation of Human Cardiac Activation Sequences"],"prefix":"10.1007","author":[{"given":"Ambre","family":"Bertrand","sequence":"first","affiliation":[]},{"given":"Julia","family":"Camps","sequence":"additional","affiliation":[]},{"given":"Vicente","family":"Grau","sequence":"additional","affiliation":[]},{"given":"Blanca","family":"Rodriguez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"E Topol","year":"2019","unstructured":"Topol, E.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019)","journal-title":"Nat. Med."},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"886723","DOI":"10.3389\/fphys.2022.886723","volume":"13","author":"M Beetz","year":"2022","unstructured":"Beetz, M.: Multi-domain variational autoencoders for combined modelling of MRI-based biventricular anatomy and ECG-based cardiac electrophysiology. Front. Physiol. 13, 886723 (2022)","journal-title":"Front. Physiol."},{"key":"22_CR3","doi-asserted-by":"publisher","first-page":"102143","DOI":"10.1016\/j.media.2021.102143","volume":"73","author":"J Camps","year":"2021","unstructured":"Camps, J.: Inference of ventricular activation properties from non-invasive electrocardiography. Med. Image Anal. 73, 102143 (2021)","journal-title":"Med. Image Anal."},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1038\/s41746-019-0193-y","volume":"2","author":"M Alber","year":"2019","unstructured":"Alber, M.: Integrating machine learning and multiscale modeling - perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digital Med. 2, 15 (2019)","journal-title":"NPJ Digital Med."},{"issue":"48","key":"22_CR5","doi-asserted-by":"publisher","first-page":"4556","DOI":"10.1093\/eurheartj\/ehaa159","volume":"41","author":"J Corral-Acero","year":"2020","unstructured":"Corral-Acero, J.: The ``Digital Twin\u2019\u2019 to enable the vision of precision cardiology. Eur. Heart J. 41(48), 4556\u20134564 (2020)","journal-title":"Eur. Heart J."},{"key":"22_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-642-21028-0_9","volume-title":"Functional Imaging and Modeling of the Heart","author":"M Wallman","year":"2011","unstructured":"Wallman, M., Smith, N., Rodriguez, B.: Estimation of activation times in cardiac tissue using graph based methods. In: Metaxas, D. N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 71\u201379. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21028-0_9"},{"key":"22_CR7","doi-asserted-by":"publisher","unstructured":"Sermesant, M., Coudi\u00e8re, Y., Moreau-Vill\u00e9ger, V., Rhode, K.S., Hill, D.L.G., Razavi, R.S.: A Fast-Marching Approach to Cardiac Electrophysiology Simulation for XMR Interventional Imaging. In: Duncan, J.S., Gerig, G. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11566489_75","DOI":"10.1007\/11566489_75"},{"key":"22_CR8","unstructured":"Tung, L.:A bi-domain model for describing ischemic myocardial d-c potentials. Dept Electr Eng Comput Sci MIT, Cambridge, MA (1978)"},{"key":"22_CR9","first-page":"191","volume":"1","author":"G Plank","year":"2017","unstructured":"Plank, G.: Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model. J. Comput. Phys. 1, 191\u2013211 (2017)","journal-title":"J. Comput. Phys."},{"key":"22_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/978-3-319-59448-4_22","volume-title":"Functional Imaging and Modelling of the Heart","author":"S Giffard-Roisin","year":"2017","unstructured":"Giffard-Roisin, S., et al.: Sparse Bayesian non-linear regression for multiple onsets estimation in non-invasive cardiac electrophysiology. In: Pop, M., Wright, G.A. (eds.) FIMH 2017. LNCS, vol. 10263, pp. 230\u2013238. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59448-4_22"},{"key":"22_CR11","unstructured":"McCarthy, A.: Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization. In: NeurIPS (2017)"},{"key":"22_CR12","first-page":"1120","volume":"12","author":"S Coveney","year":"2021","unstructured":"Coveney, S.: Bayesian calibration of electrophysiology models using restitution curve emulators. Front. Physiol. 12, 1120 (2021)","journal-title":"Front. Physiol."},{"key":"22_CR13","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3389\/fphys.2011.00014","volume":"2","author":"S Niederer","year":"2011","unstructured":"Niederer, S.: Simulating human cardiac electrophysiology on clinical time-scales. Front. Physiol. 2, 14 (2011)","journal-title":"Front. Physiol."},{"key":"22_CR14","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3389\/fphy.2020.00042","volume":"8","author":"F Costabal","year":"2020","unstructured":"Costabal, F.: Physics-informed neural networks for cardiac activation mapping. Front. Phys. 8, 42 (2020)","journal-title":"Front. Phys."},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Kashtanova, V.: APHYN-EP: physics-based deep learning framework to learn and forecast cardiac electrophysiology dynamics. In: Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, STACOM (2022)","DOI":"10.1007\/978-3-031-23443-9_18"},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.3389\/fphys.2018.01114","volume":"9","author":"K Lawson","year":"2018","unstructured":"Lawson, K.: Slow recovery of excitability increases ventricular fibrillation risk as identified by emulation. Frontiers Physiol. 9, 1114 (2018)","journal-title":"Frontiers Physiol."},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"115645","DOI":"10.1016\/j.cma.2022.115645","volume":"401","author":"D Dalton","year":"2022","unstructured":"Dalton, D.: Emulation of cardiac mechanics using Graph Neural Networks. Comput. Methods Appl. Mech. Eng. 401, 115645 (2022)","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"22_CR18","first-page":"1724","volume":"12","author":"F Meister","year":"2019","unstructured":"Meister, F.: Extrapolation of ventricular activation times from sparse electroanatomical data using graph convolutional neural networks. Front. Physiol. 12, 1724 (2019)","journal-title":"Front. Physiol."},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Suk, J.: Mesh convolutional neural networks for wall shear stress estimation in 3D artery models. In: Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge, STACOM (2021)","DOI":"10.1007\/978-3-030-93722-5_11"},{"key":"22_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/978-3-030-12029-0_24","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"S Jia","year":"2019","unstructured":"Jia, S., et al.: Automatically segmenting the left atrium from cardiac images using successive 3D U-nets and a contour loss. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 221\u2013229. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_24"},{"key":"22_CR21","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.3389\/fphys.2021.679076","volume":"12","author":"S Fresca","year":"2021","unstructured":"Fresca, S.: POD-enhanced deep learning-based reduced order models for the real-time simulation of cardiac electrophysiology in the left atrium. Front. Physiol. 12, 1431 (2021)","journal-title":"Front. Physiol."},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Desrues, G.: Towards hyper-reduction of cardiac models using poly-affine transformations In: Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, STACOM (2020)","DOI":"10.1007\/978-3-030-39074-7_11"},{"key":"22_CR23","doi-asserted-by":"publisher","first-page":"20200257","DOI":"10.1098\/rsta.2020.0257","volume":"379","author":"A Banerjee","year":"2021","unstructured":"Banerjee, A.: A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices. Philos Trans R. Soc. 379, 20200257 (2021)","journal-title":"Philos Trans R. Soc."},{"key":"22_CR24","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/BF01386390","volume":"1","author":"E Djikstra","year":"1959","unstructured":"Djikstra, E.: A note on two problems in connexion with graphs. Numer Math 1, 269\u2013271 (1959)","journal-title":"Numer Math"},{"issue":"4","key":"22_CR25","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1006\/jmcc.2000.1105","volume":"32","author":"P Taggart","year":"2000","unstructured":"Taggart, P.: Inhomogeneous transmural conduction during early ischaemia in patients with coronary artery disease. J. Mol. Cellular Cardiol. 32(4), 621\u2013630 (2000)","journal-title":"J. Mol. Cellular Cardiol."},{"key":"22_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"22_CR27","unstructured":"Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010)"},{"key":"22_CR28","unstructured":"Kingma, D.: Adam: a method for stochastic optimization. In: ICLR (2014)"}],"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\/978-3-031-35302-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T15:08:16Z","timestamp":1691593696000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35302-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031353017","9783031353024"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35302-4_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This virtual study was carried out using computer simulations which did not require ethical approval. This research has been conducted using the UK Biobank Resource under Application Number 40161. The authors express no conflict of interest. This work was funded by an Engineering and Physical Sciences Research Council doctoral award, a Wellcome Trust Fellowship in Basic Biomedical Sciences (214290\/Z\/18\/Z), the CompBioMed2 Centre of Excellence in Computational Biomedicine (European Commission Horizon 2020 research and innovation programme, grant agreement No. 823712). The computation costs were incurred through a PRACE ICEI project (icp019), which provided access to Piz Daint at the Swiss National Supercomputing Centre, Switzerland. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Considerations and Acknowledgements"}},{"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":"Lyon","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2023","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":"fimh2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/fimh2023.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Eqiunocs","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"80","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":"72","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":"90% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}