{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:31:10Z","timestamp":1742938270747,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322502"},{"type":"electronic","value":"9783030322519"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32251-9_84","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"768-776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Arterial Spin Labeling Images Synthesis via Locally-Constrained WGAN-GP Ensemble"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0541-8612","authenticated-orcid":false,"given":"Wei","family":"Huang","sequence":"first","affiliation":[]},{"given":"Mingyuan","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Huijun","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"3","key":"84_CR1","first-page":"815","volume":"37","author":"Y Huang","year":"2018","unstructured":"Huang, Y., et al.: Cross-modality image synthesis via weakly coupled and geometry co-regularized joint dictionary learning. IEEE TMI 37(3), 815\u2013827 (2018)","journal-title":"IEEE TMI"},{"issue":"3","key":"84_CR2","first-page":"755","volume":"37","author":"N Duchateau","year":"2018","unstructured":"Duchateau, N., Sermesant, M., Delingette, H., Ayache, N.: Model-based generation of large databases of cardiac images: synthesis of pathological cine MR sequences from real healthy cases. IEEE TMI 37(3), 755\u2013766 (2018)","journal-title":"IEEE TMI"},{"issue":"6","key":"84_CR3","first-page":"1328","volume":"38","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Zhou, L., Yu, B., et al.: 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE TMI 38(6), 1328\u20131339 (2018)","journal-title":"IEEE TMI"},{"issue":"3","key":"84_CR4","first-page":"741","volume":"37","author":"Y Zhou","year":"2018","unstructured":"Zhou, Y., Giffard-Roisin, S., De Craene, M., et al.: A framework for the generation of realistic synthetic cardiac ultrasound and magnetic resonance imaging sequences from the same virtual patients. IEEE TMI 37(3), 741\u2013754 (2018)","journal-title":"IEEE TMI"},{"issue":"3","key":"84_CR5","first-page":"781","volume":"37","author":"P Costa","year":"2018","unstructured":"Costa, P., Galdran, A., Meyer, M., et al.: End-to-end adversarial retinal image synthesis. IEEE TMI 37(3), 781\u2013791 (2018)","journal-title":"IEEE TMI"},{"key":"84_CR6","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. arXiv:1406.2661 (2014)"},{"key":"84_CR7","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv:1701.07875 (2017)"},{"key":"84_CR8","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., et al.: Improved training of wasserstein GANs, arXiv:1704.00028 (2017)"},{"key":"84_CR9","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.sigpro.2015.08.004","volume":"124","author":"W Huang","year":"2016","unstructured":"Huang, W.: A novel disease severity prediction scheme via big pair-wise ranking and learning techniques using image-based personal clinical data. Sign. Process. 124, 233\u2013245 (2016)","journal-title":"Sign. Process."},{"key":"84_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2906677","author":"W Huang","year":"2019","unstructured":"Huang, W., et al.: Arterial Spin labeling images synthesis from sMRI using unbalanced deep discriminant learning. IEEE TMI (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2906677","journal-title":"IEEE TMI"},{"key":"84_CR11","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.neucom.2015.07.148","volume":"204","author":"W Huang","year":"2016","unstructured":"Huang, W., Zeng, S., Wan, M., Chen, G.: Medical media analytics via ranking and big learning: a multi-modality image-based disease severity prediction study. Neurocomputing 204, 125\u2013134 (2016)","journal-title":"Neurocomputing"},{"key":"84_CR12","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., et al.: Least squares generative adversarial networks. arXiv:1611.04076 (2016)","DOI":"10.1109\/ICCV.2017.304"},{"key":"84_CR13","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., Efros, A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv:1703.10593 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"84_CR14","doi-asserted-by":"crossref","unstructured":"Rezaei, M., et al.: Conditional generative refinement adversarial networks for unbalanced medical image semantic segmentation. arXiv:1810.03871 (2018)","DOI":"10.1109\/WACV.2019.00200"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32251-9_84","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:20:45Z","timestamp":1728519645000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32251-9_84"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322502","9783030322519"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32251-9_84","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","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":"Shenzhen","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":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}