{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:29:57Z","timestamp":1759206597670,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"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_11","type":"book-chapter","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T15:04:40Z","timestamp":1642172680000},"page":"93-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Mesh Convolutional Neural Networks for\u00a0Wall Shear Stress Estimation in\u00a03D Artery Models"],"prefix":"10.1007","author":[{"given":"Julian","family":"Suk","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pim de","family":"Haan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phillip","family":"Lippe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph","family":"Brune","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jelmer M.","family":"Wolterink","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2021.694945","volume":"12","author":"XM Ferez","year":"2021","unstructured":"Ferez, X.M., et al.: Deep learning framework for real-time estimation of in-silico thrombotic risk indices in the left atrial appendage. Front. Physiol. 12, 694945 (2021)","journal-title":"Front. Physiol."},{"key":"11_CR2","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Gharleghi, R., Samarasinghe, G., Sowmya, A., Beier, S.: Deep learning for time averaged wall shear stress prediction in left main coronary bifurcations. In: IEEE: International Symposium on Biomedical Imaging, vol. 17 (2020)","DOI":"10.1109\/ISBI45749.2020.9098715"},{"key":"11_CR4","unstructured":"de Haan, P., Weiler, M., Cohen, T., Welling, M.: Gauge equivariant mesh CNNs: anisotropic convolutions on geometric graphs. In: ICLR (2021)"},{"key":"11_CR5","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Neural Information Processing Systems, vol. 30 (2017)"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"172","DOI":"10.3389\/fcvm.2019.00172","volume":"6","author":"N Hampe","year":"2019","unstructured":"Hampe, N., Wolterink, J.M., van Velzen, S.G.M., Leiner, T., I\u0161gum, I.: Machine learning for assessment of coronary artery disease in cardiac CT: a survey. Front. Cardiovasc. Med. 6, 172 (2019)","journal-title":"Front. Cardiovasc. Med."},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1093\/cvr\/cvz212","volume":"116","author":"A Hoogendoorn","year":"2019","unstructured":"Hoogendoorn, A., et al.: Multidirectional wall shear stress promotes advanced coronary plaque development: comparing five shear stress metrics. Cardiovasc. Res. 116, 1136\u20131146 (2019)","journal-title":"Cardiovasc. Res."},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1152\/japplphysiol.00752.2015","volume":"121","author":"LM Itu","year":"2016","unstructured":"Itu, L.M., et al.: A machine learning approach for computation of fractional flow reserve from coronary computed tomography. J. Appl. Physiol. 121, 42\u201352 (2016)","journal-title":"J. Appl. Physiol."},{"issue":"2","key":"11_CR9","doi-asserted-by":"publisher","first-page":"0255011","DOI":"10.1115\/1.4038751","volume":"140","author":"H Lan","year":"2018","unstructured":"Lan, H., Updegrove, A., Wilson, N.M., Maher, G.D., Shadden, S.C., Marsden, A.L.: A re-engineered software interface and workflow for the open-source SimVascular cardiovascular modeling package. J. Biomech. Eng. 140(2), 0255011\u201302450111 (2018)","journal-title":"J. Biomech. Eng."},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"20170844","DOI":"10.1098\/rsif.2017.0844","volume":"15","author":"L Liang","year":"2018","unstructured":"Liang, L., Liu, M., Martin, C., Sun, W.: A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis. J. R. Soc. Interface 15, 20170844 (2018)","journal-title":"J. R. Soc. Interface"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"109544","DOI":"10.1016\/j.jbiomech.2019.109544","volume":"99","author":"L Liang","year":"2020","unstructured":"Liang, L., Mao, W., Sun, W.: A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta. J. Biomech. 99, 109544 (2020)","journal-title":"J. Biomech."},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s12265-016-9720-2","volume":"10","author":"P Medrano-Gracia","year":"2017","unstructured":"Medrano-Gracia, P., et al.: A study of coronary bifurcation shape in a normal population. J. Cardiovasc. Transl. Res. 10, 82\u201390 (2017)","journal-title":"J. Cardiovasc. Transl. Res."},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Meister, F., et al.: Graph convolutional regression of cardiac depolarization from sparse endocardial maps. In: Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges (2021)","DOI":"10.1007\/978-3-030-68107-4_3"},{"key":"11_CR14","unstructured":"Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., Battaglia, P.: Learning mesh-based simulation with graph networks. In: International Conference on Learning Representations (2021)"},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1161\/CIRCULATIONAHA.111.021824","volume":"124","author":"H Samady","year":"2011","unstructured":"Samady, H., et al.: Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease. Circulation 124, 779\u2013778 (2011)","journal-title":"Circulation"},{"key":"11_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104038","volume":"126","author":"B Su","year":"2020","unstructured":"Su, B., Zhang, J.M., Zou, H., Ghista, D., Le, T.T., Chin, C.: Generating wall shear stress for coronary artery in real-time using neural networks: feasibility and initial results based on idealized models. Comput. Biol. Med. 126, 104038 (2020)","journal-title":"Comput. Biol. Med."},{"issue":"22","key":"11_CR17","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1016\/j.jacc.2012.11.083","volume":"61","author":"CA Taylor","year":"2013","unstructured":"Taylor, C.A., Fonte, T.A., Min, J.K.: Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve. J. Am. Coll. Cardiol. 61(22), 2233\u20132241 (2013)","journal-title":"J. Am. Coll. Cardiol."},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Verma, N., Boyer, E., Verbeek, J.: FeaStNet: feature-steered graph convolutions for 3D shape analysis. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00275"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Wolterink, J.M., Leiner, T., I\u0161gum, I.: Graph convolutional networks for coronary artery segmentation in cardiac CT angiography. In: Graph Learning in Medical Imaging (2019)","DOI":"10.1007\/978-3-030-35817-4_8"}],"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_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T14:33:50Z","timestamp":1651156430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93722-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030937218","9783030937225"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93722-5_11","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)"}}]}}