{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T15:15:17Z","timestamp":1726067717338},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811533402"},{"type":"electronic","value":"9789811533419"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-3341-9_34","type":"book-chapter","created":{"date-parts":[[2020,2,15]],"date-time":"2020-02-15T07:02:24Z","timestamp":1581750144000},"page":"416-427","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Three-Dimensional Reconstruction of Intravascular Ultrasound Images Based on Deep Learning"],"prefix":"10.1007","author":[{"given":"Yankun","family":"Cao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yushuo","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixian","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,16]]},"reference":[{"key":"34_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.01.004","volume":"37","author":"Z Gao","year":"2017","unstructured":"Gao, Z., et al.: Robust estimation of carotid artery wall motion using the elasticity-based state-space approach. Med. Image Anal. 37, 1\u201321 (2017)","journal-title":"Med. Image Anal."},{"key":"34_CR2","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/978-3-319-55994-0_19","volume-title":"Textbook of Catheter-Based Cardiovascular Interventions","author":"K Okada","year":"2018","unstructured":"Okada, K., Fitzgerald, P.J., Honda, Y.: Intravascular ultrasound. In: Lanzer, P. (ed.) Textbook of Catheter-Based Cardiovascular Interventions, pp. 329\u2013363. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-319-55994-0_19"},{"key":"34_CR3","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1016\/j.future.2019.05.009","volume":"99","author":"G Han","year":"2019","unstructured":"Han, G., et al.: Hybrid resampling and multi-feature fusion for automatic recognition of cavity imaging sign in lung CT. Future Gener. Comput. Syst. 99, 558\u2013570 (2019)","journal-title":"Future Gener. Comput. Syst."},{"key":"34_CR4","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.media.2018.09.001","volume":"50","author":"C Xu","year":"2018","unstructured":"Xu, C., et al.: Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture. Med. Image Anal. 50, 82\u201394 (2018)","journal-title":"Med. Image Anal."},{"key":"34_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-319-46723-8_13","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"N Dhungel","year":"2016","unstructured":"Dhungel, N., Carneiro, G., Bradley, A.P.: The automated learning of deep features for breast mass classification from mammograms. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 106\u2013114. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-46723-8_13"},{"key":"34_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"issue":"5","key":"34_CR7","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1109\/TMI.2016.2521800","volume":"35","author":"S Miao","year":"2016","unstructured":"Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D\/3D registration. IEEE Trans. Med. Imaging 35(5), 1352\u20131363 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Olszewski, M.E., Wahle, A., Mitchell, S.C., Sonka, M.: Segmentation of intravascular ultrasound images: a machine learning approach mimicking human vision. In: International Congress Series, vol. 1268, pp. 1045\u20131049. Elsevier (2004)","DOI":"10.1016\/j.ics.2004.03.252"},{"issue":"9","key":"34_CR9","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1016\/j.compbiomed.2006.12.003","volume":"37","author":"GD Giannoglou","year":"2007","unstructured":"Giannoglou, G.D., et al.: A novel active contour model for fully automated segmentation of intravascular ultrasound images: in vivo validation in human coronary arteries. Comput. Biol. Med. 37(9), 1292\u20131302 (2007)","journal-title":"Comput. Biol. Med."},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Mendizabal-Ruiz, G., Rivera, M., Kakadiaris, I.A.: A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587484"},{"issue":"2","key":"34_CR11","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.ultras.2010.08.001","volume":"51","author":"X Zhu","year":"2011","unstructured":"Zhu, X., Zhang, P., Shao, J., Cheng, Y., Zhang, Y., Bai, J.: A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation. Ultrasonics 51(2), 181\u2013189 (2011)","journal-title":"Ultrasonics"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Dehnavi, S.M., Babu, M.P., Yazchi, M., Basij, M.: Automatic soft and hard plaque detection in IVUS images: a textural approach. In: 2013 IEEE Conference on Information and Communication Technologies, pp. 214\u2013219. IEEE (2013)","DOI":"10.1109\/CICT.2013.6558092"},{"issue":"7","key":"34_CR13","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1016\/j.ultrasmedbio.2015.03.022","volume":"41","author":"Z Gao","year":"2015","unstructured":"Gao, Z., et al.: Automated framework for detecting lumen and mediacadventitia borders in intravascular ultrasound images. Ultrasound Med. Biol. 41(7), 2001\u20132021 (2015)","journal-title":"Ultrasound Med. Biol."},{"key":"34_CR14","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.compmedimag.2016.11.003","volume":"57","author":"S Su","year":"2017","unstructured":"Su, S., Hu, Z., Lin, Q., Hau, W.K., Gao, Z., Zhang, H.: An artificial neural network method for lumen and media-adventitia border detection in ivus. Comput. Med. Imaging Graph. 57, 29\u201339 (2017)","journal-title":"Comput. Med. Imaging Graph."},{"key":"34_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/978-3-030-04375-9_31","volume-title":"Smart Multimedia","author":"J Yang","year":"2018","unstructured":"Yang, J., Tong, L., Faraji, M., Basu, A.: IVUS-Net: an intravascular ultrasound segmentation network. In: Basu, A., Berretti, S. (eds.) ICSM 2018. LNCS, vol. 11010, pp. 367\u2013377. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-030-04375-9_31"},{"key":"34_CR16","unstructured":"Bouvrie, J.: Notes on convolutional neural networks (2006)"},{"issue":"7553","key":"34_CR17","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)","journal-title":"Nature"},{"key":"34_CR18","first-page":"276","volume":"261","author":"Y LeCun","year":"1995","unstructured":"LeCun, Y., et al.: Learning algorithms for classification: a comparison on handwritten digit recognition. Neural Netw.: Stat. Mech. Perspect. 261, 276 (1995)","journal-title":"Neural Netw.: Stat. Mech. Perspect."},{"key":"34_CR19","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"1\u20133","key":"34_CR21","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","volume":"77","author":"BC Russell","year":"2008","unstructured":"Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelME: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1\u20133), 157\u2013173 (2008)","journal-title":"Int. J. Comput. Vis."}],"container-title":["Communications in Computer and Information Science","Digital TV and Wireless Multimedia Communication"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-3341-9_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,15]],"date-time":"2020-02-15T07:06:35Z","timestamp":1581750395000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-3341-9_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811533402","9789811533419"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-3341-9_34","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"16 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IFTC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Forum on Digital TV and Wireless Multimedia Communications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iftc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.siga.com.cn\/iftc2019.html","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","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":"34","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":"28% - 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":"-","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)"}}]}}