{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T22:40:15Z","timestamp":1759704015396,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032067739"},{"type":"electronic","value":"9783032067746"}],"license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"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\/978-3-032-06774-6_21","type":"book-chapter","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T07:58:16Z","timestamp":1759564696000},"page":"277-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GReAT: Leveraging Geometric Artery Data to\u00a0Improve Wall Shear Stress Assessment"],"prefix":"10.1007","author":[{"given":"Julian","family":"Suk","sequence":"first","affiliation":[]},{"given":"Jolanda J.","family":"Wentzel","sequence":"additional","affiliation":[]},{"given":"Patryk","family":"Rygiel","sequence":"additional","affiliation":[]},{"given":"Joost","family":"Daemen","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]},{"given":"Jelmer M.","family":"Wolterink","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,5]]},"reference":[{"key":"21_CR1","unstructured":"Alkin, B., F\u00fcrst, A., Schmid, S.L., Gruber, L., Holzleitner, M., Brandstetter, J.: Universal physics transformers: a framework for efficiently scaling neural operators. In: The Thirty-Eighth Annual Conference on Neural Information Processing Systems (2024)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Candreva, A., et al.: Current and future applications of computational fluid dynamics in coronary artery disease. RCM 23(11), 377\u2013null (2022)","DOI":"10.31083\/j.rcm2311377"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Candreva, A., et al.: Risk of myocardial infarction based on endothelial shear stress analysis using coronary angiography. Atherosclerosis 342, 28\u201335 (2022)","DOI":"10.1016\/j.atherosclerosis.2021.11.010"},{"key":"21_CR4","unstructured":"Cant\u00fcrk, S., et al.: Graph positional and structural encoder. In: Proceedings of the 41st International Conference on Machine Learning. ICML\u201924, JMLR.org (2024)"},{"issue":"11","key":"21_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1145\/3131280","volume":"60","author":"K Crane","year":"2017","unstructured":"Crane, K., Weischedel, C., Wardetzky, M.: The heat method for distance computation. Commun. ACM 60(11), 90\u201399 (2017)","journal-title":"Commun. ACM"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"De\u00a0Nisco, G., et al.: Comparison of swine and human computational hemodynamics models for the study of coronary atherosclerosis. Front. Bioeng. Biotechnol. 9 (2021)","DOI":"10.3389\/fbioe.2021.731924"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Hager, P., et al.: Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat. Med. 30, 2613\u20132622 (07 2024)","DOI":"10.1038\/s41591-024-03097-1"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Hartman, E., et al.: The definition of low wall shear stress and its effect on plaque progression estimation in human coronary arteries. Sci. Rep. 11, 22086 (11 2021)","DOI":"10.1038\/s41598-021-01232-3"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Hoogendoorn, A., et al.: Multidirectional wall shear stress promotes advanced coronary plaque development: comparing five shear stress metrics. Cardiovasc. Res. 116(6), 1136\u20131146 (2019)","DOI":"10.1093\/cvr\/cvz212"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Huang, S., Xie, Y., Zhu, S.C., Zhu, Y.: Spatio-temporal self-supervised representation learning for 3D point clouds. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6515\u20136525 (2021)","DOI":"10.1109\/ICCV48922.2021.00647"},{"key":"21_CR11","unstructured":"Jaegle, A., et al.: Perceiver IO: a general architecture for structured inputs & outputs. In: International Conference on Learning Representations (2022)"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Kaissis, G., et al.: End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3, 473\u2013484 (2021)","DOI":"10.1038\/s42256-021-00337-8"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Li, G., et al.: Prediction of 3D cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning. Commun. Biol. 4 (2021)","DOI":"10.1038\/s42003-020-01638-1"},{"key":"21_CR14","unstructured":"Li, J., et al.: MedShapeNet \u2013 a large-scale dataset of 3d medical shapes for computer vision. Biomed. Eng.\/Biomed. Tech. 70(1), 71\u201390 (2025)"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Hierarchical self-supervised learning for 3D tooth segmentation in intra-oral mesh scans. IEEE Trans. Med. Imaging 42(2), 467\u2013480 (2023)","DOI":"10.1109\/TMI.2022.3222388"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15, 654 (2024)","DOI":"10.1038\/s41467-024-44824-z"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Martin, S.S., et al.: 2024 heart disease and stroke statistics: a report of US and global data from the American heart association. Circulation 149(8), e347\u2013e913 (2024)","DOI":"10.1161\/CIR.0000000000001247"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Maul, N., et al.: Transient hemodynamics prediction using an efficient octree-based deep learning model. In: Information Processing in Medical Imaging, pp. 183\u2013194. Springer Nature Switzerland, Cham (2023)","DOI":"10.1007\/978-3-031-34048-2_15"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Morales, X., et al.: Deep learning surrogate of computational fluid dynamics for thrombus formation risk in the left atrial appendage. In: Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, pp. 157\u2013166. Springer International Publishing, Cham (2020)","DOI":"10.1007\/978-3-030-39074-7_17"},{"key":"21_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108415","volume":"257","author":"G Nannini","year":"2024","unstructured":"Nannini, G., et al.: An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions. Comput. Methods Programs Biomed. 257, 108415 (2024)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"4","key":"21_CR21","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1161\/ATVBAHA.123.320337","volume":"44","author":"GD Nisco","year":"2024","unstructured":"Nisco, G.D., et al.: Predicting lipid-rich plaque progression in coronary arteries using multimodal imaging and wall shear stress signatures. Arterioscler. Thromb. Vasc. Biol. 44(4), 976\u2013986 (2024)","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"21_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107676","volume":"168","author":"L Pegolotti","year":"2024","unstructured":"Pegolotti, L., et al.: Learning reduced-order models for cardiovascular simulations with graph neural networks. Comput. Biol. Med. 168, 107676 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"21_CR23","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.3639","volume":"38","author":"MR Pfaller","year":"2022","unstructured":"Pfaller, M.R., et al.: Automated generation of 0D and 1D reduced-order models of patient-specific blood flow. Int. J. Numer. Methods Biomed. Eng. 38(10), e3639 (2022)","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"21_CR24","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, NY, USA (2017)"},{"key":"21_CR25","unstructured":"Sauder, J., Sievers, B.: Self-supervised deep learning on point clouds by reconstructing space, pp. 12962\u201312972. Curran Associates Inc., Red Hook, NY, USA (2019)"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Sharp, N., Crane, K.: A Laplacian for nonmanifold triangle meshes. Comput. Graph. Forum 39(5), 69\u201380 (2020)","DOI":"10.1111\/cgf.14069"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Suk, J., de Haan, P., Lippe, P., Brune, C., Wolterink, J.M.: Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall. Comput. Biol. Med. 173, 108328 (2024)","DOI":"10.1016\/j.compbiomed.2024.108328"},{"key":"21_CR28","unstructured":"Suk, J., Haan, P.D., Imre, B., Wolterink, J.M.: Geometric algebra transformers for large 3D meshes via cross-attention. In: ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling (2024)"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Suk, J., Imre, B., Wolterink, J.M.: LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, pp. 185\u2013195. Springer Nature Switzerland, Cham (2024)","DOI":"10.1007\/978-3-031-72390-2_18"},{"issue":"5","key":"21_CR30","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1111\/j.1467-8659.2009.01515.x","volume":"28","author":"J Sun","year":"2009","unstructured":"Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. Comput. Graph. Forum 28(5), 1383\u20131392 (2009)","journal-title":"Comput. Graph. Forum"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: Self-supervised pre-training of Swin transformers for 3D medical image analysis. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20698\u201320708 (2022)","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Valen-Sendstad, K., et al.: Real-world variability in the prediction of intracranial aneurysm wall shear stress: the 2015 international aneurysm CFD challenge. Cardiovasc. Eng. Technol. 9 (2018)","DOI":"10.1007\/s13239-018-00374-2"},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Wentzel, J.J., et al.: Sex-related differences in plaque characteristics and endothelial shear stress related plaque-progression in human coronary arteries. Atherosclerosis 342, 9\u201318 (2022)","DOI":"10.1016\/j.atherosclerosis.2021.12.013"},{"key":"21_CR34","doi-asserted-by":"publisher","unstructured":"Xie, S., Gu, J., Guo, D., Qi, C.R., Guibas, L., Litany, O.: PointContrast: unsupervised pre-training for 3D point cloud understanding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12348, pp. 574\u2013591. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_34","DOI":"10.1007\/978-3-030-58580-8_34"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Yu, X., Tang, L., Rao, Y., Huang, T., Zhou, J., Lu, J.: Point-BERT: pre-training 3D point cloud transformers with masked point modeling. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19291\u201319300 (2022)","DOI":"10.1109\/CVPR52688.2022.01871"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Girdhar, R., Joulin, A., Misra, I.: Self-supervised pretraining of 3D features on any point-cloud. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10232\u201310243 (2021)","DOI":"10.1109\/ICCV48922.2021.01009"}],"container-title":["Lecture Notes in Computer Science","Shape in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06774-6_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T22:23:15Z","timestamp":1759702995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06774-6_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,5]]},"ISBN":["9783032067739","9783032067746"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06774-6_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,5]]},"assertion":[{"value":"5 October 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\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ShapeMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Shape in Medical Imaging","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 (Democratic People's 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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"shapemi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/shapemi.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}