{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:59:50Z","timestamp":1742947190262,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031474002"},{"type":"electronic","value":"9783031474019"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-47401-9_19","type":"book-chapter","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T11:02:29Z","timestamp":1701342149000},"page":"193-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A General Computationally-Efficient 3D Reconstruction Pipeline for\u00a0Multiple Images with\u00a0Point Clouds"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9535-8424","authenticated-orcid":false,"given":"Qingyang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7866-3339","authenticated-orcid":false,"given":"Yiqing","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7459-257X","authenticated-orcid":false,"given":"Jing","family":"Ke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1038\/s41592-022-01650-9","volume":"19","author":"AL Kiemen","year":"2022","unstructured":"Kiemen, A.L., Braxton, A.M., Grahn, M.P., et al.: CODA: quantitative 3D reconstruction of large tissues at cellular resolution. Nat. Meth. 19, 1490\u20131499 (2022). https:\/\/doi.org\/10.1038\/s41592-022-01650-9","journal-title":"Nat. Meth."},{"key":"19_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-030-32254-0_64","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"R Ma","year":"2019","unstructured":"Ma, R., Wang, R., Pizer, S., Rosenman, J., McGill, S.K., Frahm, J.-M.: Real-time 3D reconstruction of colonoscopic surfaces for determining missing regions. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 573\u2013582. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_64"},{"issue":"2","key":"19_CR3","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1158\/0008-5472.CAN-21-2843","volume":"82","author":"W Xie","year":"2022","unstructured":"Xie, W., et al.: Prostate cancer risk stratification via nondestructive 3D pathology with deep learning-assisted gland analysis. Cancer Res. 82(2), 334\u2013345 (2022). https:\/\/doi.org\/10.1158\/0008-5472.CAN-21-2843","journal-title":"Cancer Res."},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Chen, C., et al.: Region proposal network with graph prior and IoU-balance loss for landmark detection in 3D ultrasound. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135 (2020). https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098368","DOI":"10.1109\/ISBI45749.2020.9098368"},{"key":"19_CR5","doi-asserted-by":"publisher","unstructured":"Wiskin, J., et al.: Full wave 3D inverse scattering transmission ultrasound tomography: breast and whole body imaging. In: 2019 IEEE International Ultrasonics Symposium (IUS), pp. 951\u2013958 (2019). https:\/\/doi.org\/10.1109\/ULTSYM.2019.8925778","DOI":"10.1109\/ULTSYM.2019.8925778"},{"key":"19_CR6","doi-asserted-by":"publisher","unstructured":"Kamencay, P., Zachariasova, M., Hudec, R., Benco, M., Radil, R.: 3D image reconstruction from 2D CT slices. In: 2014 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1\u20134 (2014). https:\/\/doi.org\/10.1109\/3DTV.2014.6874742","DOI":"10.1109\/3DTV.2014.6874742"},{"key":"19_CR7","doi-asserted-by":"publisher","unstructured":"Kermi, A., Djennelbaroud, H.C., Khadir, M.T.: A deep learning-based 3D CNN for automated Covid-19 lung lesions segmentation from 3D chest CT scans. In: 2022 5th International Symposium on Informatics and its Applications (ISIA), pp. 1\u20135 (2022). https:\/\/doi.org\/10.1109\/ISIA55826.2022.9993505","DOI":"10.1109\/ISIA55826.2022.9993505"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Ueda, D., et al.: Deep learning for MR angiography: automated detection of cerebral aneurysms. Radiology 290(1), 187\u2013194 (2019). pMID: 30351253. https:\/\/doi.org\/10.1148\/radiol.2018180901","DOI":"10.1148\/radiol.2018180901"},{"key":"19_CR9","doi-asserted-by":"publisher","unstructured":"Tang, H., Hsung, T.C., Lam, W.Y., Cheng, L.Y.Y., Pow, E.H.: On 2D\u20133D image feature detections for image-to-geometry registration in virtual dental model. In: 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 140\u2013143 (2020). https:\/\/doi.org\/10.1109\/VCIP49819.2020.9301774","DOI":"10.1109\/VCIP49819.2020.9301774"},{"key":"19_CR10","doi-asserted-by":"publisher","unstructured":"Zhang, L.z., Shen, K.: A volumetric measurement algorithm of defects in 3D CT image based on spatial intuitionistic fuzzy c-means. In: 2021 IEEE Far East NDT New Technology & Application Forum (FENDT), pp. 78\u201382 (2021). https:\/\/doi.org\/10.1109\/FENDT54151.2021.9749668","DOI":"10.1109\/FENDT54151.2021.9749668"},{"key":"19_CR11","doi-asserted-by":"publisher","unstructured":"Leonardi, V., Vidal, V., Mari, J.L., Daniel, M.: 3D reconstruction from CT-scan volume dataset application to kidney modeling. In: Proceedings of the 27th Spring Conference on Computer Graphics, SCCG 2011, pp. 111\u2013120. Association for Computing Machinery, New York (2011). https:\/\/doi.org\/10.1145\/2461217.2461239","DOI":"10.1145\/2461217.2461239"},{"key":"19_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-030-87193-2_4","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"JMJ Valanarasu","year":"2021","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 36\u201346. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_4"},{"key":"19_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/978-3-030-58548-8_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Wang","year":"2020","unstructured":"Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., Chen, L.-C.: Axial-DeepLab: stand-alone axial-attention for panoptic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 108\u2013126. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_7"},{"issue":"1","key":"19_CR14","doi-asserted-by":"publisher","first-page":"16878","DOI":"10.1038\/s41598-017-17204-5","volume":"7","author":"P Bankhead","year":"2017","unstructured":"Bankhead, P., et al.: QuPath: open source software for digital pathology image analysis. Sci. Rep. 7(1), 16878 (2017). https:\/\/doi.org\/10.1038\/s41598-017-17204-5","journal-title":"Sci. Rep."},{"key":"19_CR15","doi-asserted-by":"publisher","unstructured":"Chauhan, I., Rawat, A., Chauhan, M., Garg, R.: Fusion of low-cost UAV point cloud with TLS point cloud for complete 3D visualisation of a building. In: 2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), pp. 234\u2013237 (2021). https:\/\/doi.org\/10.1109\/InGARSS51564.2021.9792104","DOI":"10.1109\/InGARSS51564.2021.9792104"},{"key":"19_CR16","doi-asserted-by":"publisher","unstructured":"Chen, M., Miao, Y., Gong, Y., Mao, X.: Convolutional neural network powered identification of the location and orientation of human body via human form point cloud. In: 2021 15th European Conference on Antennas and Propagation (EuCAP), pp. 1\u20135 (2021). https:\/\/doi.org\/10.23919\/EuCAP51087.2021.9410980","DOI":"10.23919\/EuCAP51087.2021.9410980"},{"key":"19_CR17","doi-asserted-by":"publisher","unstructured":"Wen, Z., Yan, Y., Cui, H.: Study on segmentation of 3D human body based on point cloud data. In: 2012 Second International Conference on Intelligent System Design and Engineering Application, pp. 657\u2013660 (2012). https:\/\/doi.org\/10.1109\/ISdea.2012.676","DOI":"10.1109\/ISdea.2012.676"},{"issue":"5","key":"19_CR18","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1109\/TPAMI.1987.4767965","volume":"9","author":"KS Arun","year":"1987","unstructured":"Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 9(5), 698\u2013700 (1987). https:\/\/doi.org\/10.1109\/TPAMI.1987.4767965","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (PAMI)"},{"key":"19_CR19","unstructured":"Zhou, Q.Y., Park, J., Koltun, V.: Open3D: a modern library for 3D data processing. arXiv:1801.09847 (2018)"},{"key":"19_CR20","doi-asserted-by":"publisher","unstructured":"Alsaid, B., et al.: Coexistence of adrenergic and cholinergic nerves in the inferior hypogastric plexus: anatomical and immunohistochemical study with 3D reconstruction in human male fetus. J. Anat. 214(5), 645\u2013654 (2009). https:\/\/doi.org\/10.1111\/j.1469-7580.2009.01071.x. https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/j.1469-7580.2009.01071.x","DOI":"10.1111\/j.1469-7580.2009.01071.x"},{"key":"19_CR21","doi-asserted-by":"publisher","unstructured":"Karam, I., Droupy, S., Abd-Alsamad, I., Uhl, J.F., Beno\u00eet, G., Delmas, V.: Innervation of the female human urethral sphincter: 3D reconstruction of immunohistochemical studies in the fetus. Eur. Urol. 47(5), 627\u2013634 (2005). https:\/\/doi.org\/10.1016\/j.eururo.2005.01.001. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0302283805000060","DOI":"10.1016\/j.eururo.2005.01.001"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47401-9_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T11:05:36Z","timestamp":1701342336000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47401-9_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031474002","9783031474019"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47401-9_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"2250","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":"730","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":"32% - 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":"5","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)"}}]}}