{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T04:02:43Z","timestamp":1750478563254,"version":"3.41.0"},"publisher-location":"Cham","reference-count":20,"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_24","type":"book-chapter","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T10:06:55Z","timestamp":1750414015000},"page":"266-274","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Thoracic Aorta Segmentation from 4D Flow MRI: Methods Comparison"],"prefix":"10.1007","author":[{"given":"Tom","family":"Da Silva-Faria","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alban","family":"Redheuil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas","family":"Leite","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louis","family":"Parker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan-Anh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khaoula","family":"Bouazizi-Verdier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Dietenbeck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Bouaou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophia","family":"Houriez-Gombaud-Saintonge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umit","family":"Gencer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elie","family":"Mousseaux","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gilles","family":"Soulat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emilie","family":"Bollache","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadjia","family":"Kachenoura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"issue":"3","key":"24_CR1","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1002\/jmri.26673","volume":"50","author":"K Bouaou","year":"2019","unstructured":"Bouaou, K., et al.: Analysis of aortic pressure fields from 4D flow MRI in healthy volunteers: associations with age and left ventricular remodeling. J. Magn. Reson. Imaging (JMRI) 50(3), 982\u2013993 (2019). https:\/\/doi.org\/10.1002\/jmri.26673","journal-title":"J. Magn. Reson. Imaging (JMRI)"},{"issue":"1","key":"24_CR2","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s12968-019-0584-x","volume":"21","author":"S Houriez-Gombaud-Saintonge","year":"2019","unstructured":"Houriez-Gombaud-Saintonge, S., et al.: Comparison of different methods for the estimation of aortic pulse wave velocity from 4D flow cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. (JCMR) 21(1), 75 (2019). https:\/\/doi.org\/10.1186\/s12968-019-0584-x","journal-title":"J. Cardiovasc. Magn. Reson. (JCMR)"},{"issue":"3","key":"24_CR3","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1093\/ehjci\/jead283","volume":"25","author":"C Trenti","year":"2024","unstructured":"Trenti, C., et al.: Oscillatory shear stress is elevated in patients with bicuspid aortic valve and aortic regurgitation: a 4D flow cardiovascular magnetic resonance cross-sectional study. Eur. Heart J. Cardiovasc. Imaging 25(3), 404\u2013412 (2024). https:\/\/doi.org\/10.1093\/ehjci\/jead283","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"issue":"2","key":"24_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocmr.2024.101081","volume":"26","author":"C Manini","year":"2024","unstructured":"Manini, C., et al.: Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging. JCMR 26(2), 101081 (2024). https:\/\/doi.org\/10.1016\/j.jocmr.2024.101081","journal-title":"JCMR"},{"key":"24_CR5","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.mri.2017.11.010","volume":"47","author":"M Codari","year":"2018","unstructured":"Codari, M., et al.: Fully automated contour detection of the ascending aorta in cardiac 2D phase-contrast MRI. MRI 47, 77\u201382 (2018). https:\/\/doi.org\/10.1016\/j.mri.2017.11.010","journal-title":"MRI"},{"issue":"1","key":"24_CR6","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1002\/jmri.24338","volume":"40","author":"A Goel","year":"2014","unstructured":"Goel, A., et al.: A fully automated tool to identify the aorta and compute flow using Phase-contrast MRI: validation and application in a large population based study. JMRI 40(1), 221\u2013228 (2014). https:\/\/doi.org\/10.1002\/jmri.24338","journal-title":"JMRI"},{"issue":"1","key":"24_CR7","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.mri.2014.08.019","volume":"33","author":"RV Bergen","year":"2015","unstructured":"Bergen, R.V., et al.: 4D MR phase and magnitude segmentations with GPU parallel computing. MRI 33(1), 134\u2013145 (2015). https:\/\/doi.org\/10.1016\/j.mri.2014.08.019","journal-title":"MRI"},{"issue":"2","key":"24_CR8","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1002\/mrm.25630","volume":"75","author":"P Volonghi","year":"2016","unstructured":"Volonghi, P., et al.: Automatic extraction of three-dimensional thoracic aorta geometric model from phase contrast MRI for morphometric and hemodynamic characterization. Magn. Reson. Med. 75(2), 873\u2013882 (2016). https:\/\/doi.org\/10.1002\/mrm.25630","journal-title":"Magn. Reson. Med."},{"issue":"4","key":"24_CR9","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1002\/mrm.28257","volume":"84","author":"H Berhane","year":"2020","unstructured":"Berhane, H., et al.: Fully automated 3D aortic segmentation of 4D flow MRI for hemodynamic analysis using deep learning. Magn. Reson. Med. 84(4), 2204\u20132218 (2020). https:\/\/doi.org\/10.1002\/mrm.28257","journal-title":"Magn. Reson. Med."},{"key":"24_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocmr.2024.100879","volume":"26","author":"C Trenti","year":"2024","unstructured":"Trenti, C., et al.: Automatic time-resolved multi-label segmentation of the aorta FBom 4D flow CMR. JCMR 26, 100879 (2024). https:\/\/doi.org\/10.1016\/j.jocmr.2024.100879","journal-title":"JCMR"},{"key":"24_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.mri.2022.12.021","volume":"99","author":"DM Marin-Castrillon","year":"2023","unstructured":"Marin-Castrillon, D.M., et al.: 4D segmentation of the thoracic aorta from 4D flow MRI using deep learning. MRI 99, 20\u201325 (2023). https:\/\/doi.org\/10.1016\/j.mri.2022.12.021","journal-title":"MRI"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1002\/jmri.29236","volume":"60","author":"J Guo","year":"2024","unstructured":"Guo, J., et al.: Deep learning-based analysis of aortic morphology from three-dimensional MRI. JMRI 60, 1565\u20131576 (2024). https:\/\/doi.org\/10.1002\/jmri.29236","journal-title":"JMRI"},{"issue":"2","key":"24_CR13","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., et al.: 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","journal-title":"Nat. Methods"},{"issue":"10","key":"24_CR14","doi-asserted-by":"publisher","first-page":"7117","DOI":"10.1007\/s00330-022-09068-9","volume":"32","author":"J Garrido-Oliver","year":"2022","unstructured":"Garrido-Oliver, J., et al.: Machine learning for the automatic assessment of aortic rotational flow and wall shear stress from 4D flow cardiac magnetic resonance imaging. Eur. Radiol. 32(10), 7117\u20137127 (2022). https:\/\/doi.org\/10.1007\/s00330-022-09068-9","journal-title":"Eur. Radiol."},{"issue":"1","key":"24_CR15","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s12968-023-00942-z","volume":"25","author":"M Bissell","year":"2023","unstructured":"Bissell, M., et al.: 4D Flow cardiovascular MR consensus statement: 2023 update. JCMR 25(1), 40 (2023). https:\/\/doi.org\/10.1186\/s12968-023-00942-z","journal-title":"JCMR"},{"issue":"9","key":"24_CR16","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.diii.2023.04.004","volume":"104","author":"T Dietenbeck","year":"2023","unstructured":"Dietenbeck, T., et al.: Value of aortic volumes assessed by automated segmentation of 3D MRI data in patients with thoracic aortic dilatation: a case-control study. Diagn. Interv. Imaging 104(9), 419\u2013426 (2023). https:\/\/doi.org\/10.1016\/j.diii.2023.04.004","journal-title":"Diagn. Interv. Imaging"},{"issue":"41","key":"24_CR17","doi-asserted-by":"publisher","first-page":"2873","DOI":"10.1093\/eurheartj\/ehu281","volume":"35","author":"R Erbel","year":"2014","unstructured":"Erbel, R., et al.: 2014 ESC Guidelines on the diagnosis and treatment of aortic diseases. Eur. Heart J. 35(41), 2873\u20132926 (2014). https:\/\/doi.org\/10.1093\/eurheartj\/ehu281","journal-title":"Eur. Heart J."},{"issue":"8","key":"24_CR18","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2897538","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"24_CR19","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1007\/s10554-016-0938-5","volume":"32","author":"E Bollache","year":"2016","unstructured":"Bollache, E., van Ooij, P., Powell, A., Carr, J., Markl, M., Barker, A.J.: Comparison of 4D flow and 2D velocity-encoded phase contrast MRI sequences for the evaluation of aortic hemodynamics. JCI 32(10), 1529\u20131541 (2016). https:\/\/doi.org\/10.1007\/s10554-016-0938-5","journal-title":"JCI"},{"key":"24_CR20","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1002\/jmri.26007","volume":"48","author":"J Zimmermann","year":"2018","unstructured":"Zimmermann, J., et al.: Wall shear stress estimation in the aorta: Impact of wall motion, spatiotemporal resolution, and phase noise. JMRI 48, 718\u2013728 (2018). https:\/\/doi.org\/10.1002\/jmri.26007","journal-title":"JMRI"}],"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_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T10:06:56Z","timestamp":1750414016000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94562-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031945618","9783031945625"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94562-5_24","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":"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"}}]}}