{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:06:56Z","timestamp":1779062816921,"version":"3.51.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032177339","type":"print"},{"value":"9783032177346","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-17734-6_22","type":"book-chapter","created":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T23:38:10Z","timestamp":1779061090000},"page":"224-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Segmentation of\u00a03D Left Atrial Meshes from\u00a0Electroanatomical Mapping"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6974-7951","authenticated-orcid":false,"given":"Irene","family":"Mancebo-Laguna","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9792-8769","authenticated-orcid":false,"given":"Pablo \u00c1vila","family":"Alonso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5023-8215","authenticated-orcid":false,"given":"Roberto G\u00f3mez","family":"S\u00e1nchez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4779-2492","authenticated-orcid":false,"given":"Mar\u00eda Mu\u00f1oz","family":"P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcos Albarr\u00e1n","family":"G\u00f3mez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4059-8230","authenticated-orcid":false,"given":"Alejandro","family":"Carta-Bergaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felipe","family":"Atienza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6636-5473","authenticated-orcid":false,"given":"\u00c1ngel","family":"Arenal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2446-3045","authenticated-orcid":false,"given":"Gonzalo R.","family":"R\u00edos-Mu\u00f1oz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Bieging, E.T., Morris, A., Chang, L., Dagher, L., Marrouche, N.F., Cates, J.: Statistical shape analysis of the left atrial appendage predicts stroke in atrial fibrillation. The international journal of cardiovascular imaging 37(8), 2521\u20132527 (2021). 120.1007\/s10554-021-02262-8","DOI":"10.1007\/s10554-021-02262-8"},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Bonneau, D., Difrancesco, P.M., Hutchinson, D.: Surface reconstruction for three-dimensional rockfall volumetric analysis. ISPRS Int. J. Geo-Inform. 8, 548 (2019). https:\/\/doi.org\/10.3390\/ijgi8120548","DOI":"10.3390\/ijgi8120548"},{"issue":"11","key":"22_CR3","doi-asserted-by":"publisher","first-page":"3679","DOI":"10.1109\/TMI.2020.3002417","volume":"39","author":"T Eelbode","year":"2020","unstructured":"Eelbode, T., et al.: Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index. IEEE Trans. Med. Imaging 39(11), 3679\u20133690 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.3002417","journal-title":"IEEE Trans. Med. Imaging"},{"key":"22_CR4","doi-asserted-by":"publisher","unstructured":"Engel, N., Belagiannis, V., Dietmayer, K.: Point transformer. IEEE Access 9, 134826\u2013134840 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3116304","DOI":"10.1109\/ACCESS.2021.3116304"},{"key":"22_CR5","doi-asserted-by":"publisher","unstructured":"Hanocka, R., et\u00a0al.: Meshcnn: a network with an edge. ACM Trans. Graph. 38(4), July 2019. https:\/\/doi.org\/10.1145\/3306346.3322959","DOI":"10.1145\/3306346.3322959"},{"key":"22_CR6","doi-asserted-by":"publisher","unstructured":"Hansen, B., et\u00a0al.: Sparsemeshcnn with self-attention for segmentation of large meshes. Proceedings of the Northern Lights Deep Learning Workshop 3 (2022). https:\/\/doi.org\/10.7557\/18.6281","DOI":"10.7557\/18.6281"},{"issue":"10","key":"22_CR7","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1056\/NEJM199809033391003","volume":"339","author":"M Ha\u00efssaguerre","year":"1998","unstructured":"Ha\u00efssaguerre, M., et al.: Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N. Engl. J. Med. 339(10), 659\u2013666 (1998). https:\/\/doi.org\/10.1056\/NEJM199809033391003","journal-title":"N. Engl. J. Med."},{"key":"22_CR8","doi-asserted-by":"publisher","unstructured":"Hosseini, S.M., et\u00a0al.: Catheter ablation for cardiac arrhythmias: utilization and in-hospital complications, 2000 to 2013. JACC: Clinical Electrophysiol. 3(11), 1240\u20131248 (2017). https:\/\/doi.org\/10.1016\/j.jacep.2017.05.005","DOI":"10.1016\/j.jacep.2017.05.005"},{"key":"22_CR9","doi-asserted-by":"publisher","unstructured":"Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts (08 2016). https:\/\/doi.org\/10.48550\/arXiv.1608.03983","DOI":"10.48550\/arXiv.1608.03983"},{"key":"22_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrthm.2025.03.1955","author":"M Narita","year":"2025","unstructured":"Narita, M., et al.: Comparison of the characteristics between machine learning and deep learning algorithms for ablation site classification in a novel cloud-based system. Heart Rhythm (2025). https:\/\/doi.org\/10.1016\/j.hrthm.2025.03.1955","journal-title":"Heart Rhythm"},{"key":"22_CR11","doi-asserted-by":"publisher","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 165\u2013174 (2019). https:\/\/doi.org\/10.48550\/arXiv.1901.05103","DOI":"10.48550\/arXiv.1901.05103"},{"key":"22_CR12","doi-asserted-by":"publisher","unstructured":"Prasanna, L., Praveena, R., AS, D., Kumar, M.: Variations in the pulmonary venous ostium in the left atrium and its clinical importance. J. Clin. Diagnostic Research: JCDR 8(2), 10 (2014). https:\/\/doi.org\/10.7860\/JCDR\/2014\/7649.3992","DOI":"10.7860\/JCDR\/2014\/7649.3992"},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5105\u20135114. NIPS\u201917. Curran Associates Inc., Red Hook (2017). https:\/\/doi.org\/10.48550\/arXiv.1706.02413","DOI":"10.48550\/arXiv.1706.02413"},{"key":"22_CR14","doi-asserted-by":"publisher","unstructured":"Qi, Y., Hu, C., Zuo, L., Yang, B., Lv, Y.: Cardiac magnetic resonance image segmentation method based on multi-scale feature fusion and sequence relationship learning. Sensors 23(2) (2023).https:\/\/doi.org\/10.3390\/s23020690","DOI":"10.3390\/s23020690"},{"issue":"3","key":"22_CR15","doi-asserted-by":"publisher","first-page":"20230038","DOI":"10.1098\/rsfs.2023.0038","volume":"13","author":"CH Roney","year":"2023","unstructured":"Roney, C.H., et al.: Constructing bilayer and volumetric atrial models at scale. Interface Focus 13(3), 20230038 (2023). https:\/\/doi.org\/10.1098\/rsfs.2023.0038","journal-title":"Interface Focus"},{"key":"22_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.819429","volume":"9","author":"GR R\u00edos-Mu\u00f1oz","year":"2022","unstructured":"R\u00edos-Mu\u00f1oz, G.R., et al.: Structural remodeling and rotational activity in persistent\/long-lasting atrial fibrillation: Gender-effect differences and impact on post-ablation outcome. Front. Cardiovascular Med. 9, 819429 (2022). https:\/\/doi.org\/10.3389\/fcvm.2022.819429","journal-title":"Front. Cardiovascular Med."},{"key":"22_CR17","doi-asserted-by":"publisher","unstructured":"Schneider, L., Niemann, A., Beuing, O., Preim, B., Saalfeld, S.: Medmeshcnn - enabling meshcnn for medical surface models. Comput. Methods Programs Biomed. 210, 106372 (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2021.106372","DOI":"10.1016\/j.cmpb.2021.106372"},{"key":"22_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107009","volume":"162","author":"JA Sol\u00eds-Lemus","year":"2023","unstructured":"Sol\u00eds-Lemus, J.A., et al.: Evaluation of an open-source pipeline to create patient-specific left atrial models: a reproducibility study. Comput. Biol. Med. 162, 107009 (2023). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107009","journal-title":"Comput. Biol. Med."},{"key":"22_CR19","doi-asserted-by":"publisher","unstructured":"S\u00f8rensen, K., ohters: Spatio-temporal neural distance fields for conditional generative modeling of the heart. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, pp. 422\u2013432. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72384-1_40","DOI":"10.1007\/978-3-031-72384-1_40"},{"key":"22_CR20","doi-asserted-by":"publisher","unstructured":"Tzeis, S., et\u00a0al.: 2024 European Heart Rhythm Association\/Heart Rhythm Society\/Asia Pacific Heart Rhythm Society\/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation. Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing 26(4) (4 2024). https:\/\/doi.org\/10.1007\/s10840-024-01771-5","DOI":"10.1007\/s10840-024-01771-5"},{"key":"22_CR21","unstructured":"Veli\u010dkovi\u0107, P., et\u00a0al.: Graph attention networks. In: International Conference on Learning Representations (ICLR) (2018). https:\/\/arxiv.org\/abs\/1710.10903"},{"key":"22_CR22","doi-asserted-by":"publisher","unstructured":"Wang, Y., et\u00a0al.: Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (tog) 38(5), 1\u201312 (2019). https:\/\/doi.org\/10.48550\/arXiv.1801.07829","DOI":"10.48550\/arXiv.1801.07829"},{"key":"22_CR23","doi-asserted-by":"publisher","unstructured":"Wong, G.R., et\u00a0al.: Sex-related differences in atrial remodeling in patients with atrial fibrillation: Relationship to ablation outcomes. Circulation: Arrhythmia Electrophysiol. 15(1), e009925 (2022). https:\/\/doi.org\/10.1161\/CIRCEP.121.009925","DOI":"10.1161\/CIRCEP.121.009925"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Z.: Iterative closest point (icp). In: Computer vision: a reference guide, pp. 718\u2013720. Springer (2021)","DOI":"10.1007\/978-3-030-63416-2_179"}],"container-title":["Lecture Notes in Computer Science","Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-17734-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T23:38:12Z","timestamp":1779061092000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-17734-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032177339","9783032177346"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-17734-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"P\u00c1 received honoraria for teaching from Medtronic Inc. and served on the Advisory Board for Boston Scientific Corp. \u00c1A received consultancy fees from Boston Scientific Corp. and Medtronic Inc. The other authors declare that the research was carried out without any commercial or financial relationships that could be seen as a potential conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stacom2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/stacom.github.io\/stacom2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}