{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T06:18:36Z","timestamp":1769840316446,"version":"3.49.0"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030875886","type":"print"},{"value":"9783030875893","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87589-3_59","type":"book-chapter","created":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T07:02:35Z","timestamp":1632553355000},"page":"576-585","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["CorLab-Net: Anatomical Dependency-Aware Point-Cloud Learning for Automatic Labeling of Coronary Arteries"],"prefix":"10.1007","author":[{"given":"Xiao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanli","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dijia","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"12","key":"59_CR1","doi-asserted-by":"publisher","first-page":"4322","DOI":"10.1109\/TMI.2020.3017275","volume":"39","author":"D Zhang","year":"2020","unstructured":"Zhang, D., et al.: Direct quantification of coronary artery stenosis through hierarchical attentive multi-view learning. IEEE Trans. Med. Imaging 39(12), 4322\u20134334 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"59_CR2","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1109\/TMI.2002.801151","volume":"21","author":"SY Chen","year":"2002","unstructured":"Chen, S.Y., Carroll, J.D., Messenger, J.C.: Quantitative analysis of reconstructed 3-D coronary arterial tree and intracoronary devices. IEEE Trans. Med. Imaging 21(7), 724\u2013740 (2002)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"59_CR3","unstructured":"Yang, G., et al.: Automatic coronary artery tree labeling in coronary computed tomographic angiography datasets. In: Computing in Cardiology, vol. 2011, pp. 109\u2013112. IEEE (2011)"},{"issue":"11","key":"59_CR4","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1007\/s10554-017-1169-0","volume":"33","author":"Q Cao","year":"2017","unstructured":"Cao, Q., et al.: Automatic identification of coronary tree anatomy in coronary computed tomography angiography. Int. J. Cardiovasc. Imaging 33(11), 1809\u20131819 (2017)","journal-title":"Int. J. Cardiovasc. Imaging"},{"issue":"2","key":"59_CR5","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s11548-018-1884-6","volume":"14","author":"D Wu","year":"2019","unstructured":"Wu, D., et al.: Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs. Int. J. Comput. Assist. Radiol. Surg. 14(2), 271\u2013280 (2019)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"59_CR6","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"key":"59_CR7","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 (2017)"},{"key":"59_CR8","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, H., Hu, Q., et al.: Deep learning for 3D point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2020)","DOI":"10.1109\/TPAMI.2021.3070341"},{"key":"59_CR9","unstructured":"Yang, X., Xia, D., Kin, T., Igarashi, T.: Surface-based 3D deep learning framework for segmentation of intracranial aneurysms from TOF-MRA images. arXiv preprint arXiv:2006.16161 (2020)"},{"key":"59_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-030-59861-7_48","volume-title":"Machine Learning in Medical Imaging","author":"L Yao","year":"2020","unstructured":"Yao, L., et al.: Graph convolutional network based point cloud for head and neck vessel labeling. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds.) MLMI 2020. LNCS, vol. 12436, pp. 474\u2013483. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59861-7_48"},{"key":"59_CR11","doi-asserted-by":"crossref","unstructured":"Wang, S., Dai, W., Xu, M., Li, C., Zou, J., Xiong, H.: Structure-aware graph construction for point cloud segmentation with graph convolutional networks. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/ICME46284.2020.9102893"},{"key":"59_CR12","doi-asserted-by":"crossref","unstructured":"Lin, Z.H., Huang, S.Y., Wang, Y.C.F.: Convolution in the cloud: learning deformable kernels in 3D graph convolution networks for point cloud analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1800\u20131809 (2020)","DOI":"10.1109\/CVPR42600.2020.00187"},{"key":"59_CR13","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhen, X., Chi, Y., Zhang, L., Hua, X.S.: CPR-GCN: conditional partial-residual graph convolutional network in automated anatomical labeling of coronary arteries. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3803\u20133811 (2020)","DOI":"10.1109\/CVPR42600.2020.00386"},{"key":"59_CR14","doi-asserted-by":"crossref","unstructured":"Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 605\u2013613 (2017)","DOI":"10.1109\/CVPR.2017.264"},{"key":"59_CR15","doi-asserted-by":"crossref","unstructured":"Cui, Z., et al.: TsegNet: an efficient and accurate tooth segmentation network on 3D dental model. Med. Image Anal. 69, 101949 (2021)","DOI":"10.1016\/j.media.2020.101949"},{"key":"59_CR16","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87589-3_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,10]],"date-time":"2022-04-10T15:14:18Z","timestamp":1649603658000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87589-3_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030875886","9783030875893"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87589-3_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"mlmi-med2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2021\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"92","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":"71","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":"77% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The workshop was held virtually.","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)"}}]}}