{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T16:41:00Z","timestamp":1776271260947,"version":"3.50.1"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031532405","type":"print"},{"value":"9783031532412","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53241-2_1","type":"book-chapter","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T07:02:40Z","timestamp":1707462160000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["M3F: Multi-Field-of-View Feature Fusion Network for\u00a0Aortic Vessel Tree Segmentation in\u00a0CT Angiography"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4263-6414","authenticated-orcid":false,"given":"Yunsu","family":"Byeon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9849-0719","authenticated-orcid":false,"given":"Hyeseong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4760-8886","authenticated-orcid":false,"given":"Kyungwon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6237-468X","authenticated-orcid":false,"given":"Doohyun","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5207-0911","authenticated-orcid":false,"given":"Euijoon","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2217-2837","authenticated-orcid":false,"given":"Dosik","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,10]]},"reference":[{"key":"1_CR1","unstructured":"Miccai 2023 Seg.A. challenge. https:\/\/multicenteraorta.grand-challenge.org\/"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks. Data Brief 40, 107801 (2022)","DOI":"10.1016\/j.dib.2022.107801"},{"issue":"2","key":"1_CR3","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"1_CR4","unstructured":"Jin, Y., et al.: AI-based aortic vessel tree segmentation for cardiovascular diseases treatment: status quo (2023)"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Jun, Y., et al.: Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning. Eur. Radiol., 1\u201310 (2023)","DOI":"10.1007\/s00330-023-09590-4"},{"issue":"2","key":"1_CR6","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2019","unstructured":"Karimi, D., Salcudean, S.E.: Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imaging 39(2), 499\u2013513 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1_CR8","unstructured":"Ma, J., et al.: How distance transform maps boost segmentation CNNs: an empirical study. In: Medical Imaging with Deep Learning, pp. 479\u2013492. PMLR (2020)"},{"key":"1_CR9","unstructured":"Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arxiv 2018. arXiv preprint arXiv:1804.03999 (1804)"},{"issue":"1","key":"1_CR10","doi-asserted-by":"publisher","first-page":"13420","DOI":"10.1038\/s41598-023-40506-w","volume":"13","author":"D Park","year":"2023","unstructured":"Park, D., et al.: Development and validation of a hybrid deep learning-machine learning approach for severity assessment of covid-19 and other pneumonias. Sci. Rep. 13(1), 13420 (2023)","journal-title":"Sci. Rep."},{"issue":"12","key":"1_CR11","doi-asserted-by":"publisher","first-page":"8716","DOI":"10.1007\/s00330-022-08869-2","volume":"32","author":"D Park","year":"2022","unstructured":"Park, D., et al.: Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer. Eur. Radiol. 32(12), 8716\u20138725 (2022)","journal-title":"Eur. Radiol."},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"6686","DOI":"10.1007\/s00330-021-07783-3","volume":"31","author":"YW Park","year":"2021","unstructured":"Park, Y.W., et al.: Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging. Eur. Radiol. 31, 6686\u20136695 (2021)","journal-title":"Eur. Radiol."},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Pepe, A., et al.: Detection, segmentation, simulation and visualization of aortic dissections: a review. Med. Image Anal. 65, 101773 (2020)","DOI":"10.1016\/j.media.2020.101773"},{"key":"1_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Shin, H., Kim, H., Kim, S., Jun, Y., Eo, T., Hwang, D.: SDC-UDA: volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7412\u20137421 (2023)","DOI":"10.1109\/CVPR52729.2023.00716"},{"key":"1_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101890","volume":"68","author":"M Van Rijthoven","year":"2021","unstructured":"Van Rijthoven, M., Balkenhol, M., Sili\u0146a, K., Van Der Laak, J., Ciompi, F.: HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images. Med. Image Anal. 68, 101890 (2021)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Segmentation of the Aorta. Towards the Automatic Segmentation, Modeling, and Meshing of the Aortic Vessel Tree from Multicenter Acquisition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53241-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T07:02:50Z","timestamp":1707462170000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53241-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031532405","9783031532412"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53241-2_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"10 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SEGA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Challenge on Segmentation of the Aorta","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sega2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/multicenteraorta.grand-challenge.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","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":"8","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":"3","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":"73% - 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":"3","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":"1","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)"}}]}}