{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:32:18Z","timestamp":1743082338831,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819958467"},{"type":"electronic","value":"9789819958474"}],"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\/978-981-99-5847-4_7","type":"book-chapter","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T20:30:00Z","timestamp":1693341000000},"page":"90-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Deep Learning Algorithm for Synthesizing Magnetic Resonance Images from Spine Computed Tomography Images using Mixed Loss Functions"],"prefix":"10.1007","author":[{"given":"Rizhong","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Menghua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijie","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"issue":"11","key":"7_CR1","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial networks. Commun. ACM. 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM."},{"doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cy-cle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vsion (ICCV), pp. 2223\u20132232 (2017)","key":"7_CR2","DOI":"10.1109\/ICCV.2017.244"},{"unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv: 1511.06434 (2015)","key":"7_CR3"},{"doi-asserted-by":"crossref","unstructured":"Kaneko, T., Kameoka, H., Tanaka, K., et al.: Cyclegan-vc2: improved cyclegan-based non-parallel voice conversion. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6820\u20136824 (2019)","key":"7_CR4","DOI":"10.1109\/ICASSP.2019.8682897"},{"unstructured":"Karras, T., Aila, T., Laine, S., et al.: Progressive growing of gans for improved quality, stability, and variation. arXiv preprint 1710.10196 (2018)","key":"7_CR5"},{"unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv: 1411.1784 (2014)","key":"7_CR6"},{"unstructured":"Yoon, D., Oh, J., Choi, H., et al.: OUR-GAN: One-shot Ultra-high-Resolution Generative Adversarial Networks. arXiv preprint arXiv: 2202.13799 (2022)","key":"7_CR7"},{"unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., et al.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, pp.1\u201312 (2017)","key":"7_CR8"},{"doi-asserted-by":"crossref","unstructured":"Bynagari, N.B.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Asian Journal of Applied Science and Engineering (AJASE). 8, 25-34 (2019)","key":"7_CR9","DOI":"10.18034\/ajase.v8i1.9"},{"unstructured":"Sato, N., Iiduka, H.: Using constant learning rate of two time-scale update rule for training generative adversarial networks. arXiv preprint arXiv: 2201.11989 (2022)","key":"7_CR10"},{"doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., et al.: High-resolution image synthesis and semantic manipulation with conditional gans. In: Proceedings of the IEEE Conference on Com-puter Vision and Pattern Recognition, pp. 8798\u20138807. IEEE (2018)","key":"7_CR11","DOI":"10.1109\/CVPR.2018.00917"},{"doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, pp. 234\u2013241 (2015)","key":"7_CR12","DOI":"10.1007\/978-3-319-24574-4_28"},{"doi-asserted-by":"crossref","unstructured":"Zunair, H., Hamza, A.B.: Sharp U-Net: depthwise convolutional network for biomedical image segmentation. Comput. Biol. Med. (CIBM) 136, 104699 (2021)","key":"7_CR13","DOI":"10.1016\/j.compbiomed.2021.104699"},{"doi-asserted-by":"crossref","unstructured":"Shaziya, H., Shyamala, K., Zaheer, R.: Automatic lung segmentation on thoracic CT scans using U-net convolutional network. In: 2018 International Conference on Com-munication and Signal Processing (ICCSP), pp. 0643\u20130647 (2018)","key":"7_CR14","DOI":"10.1109\/ICCSP.2018.8524484"},{"unstructured":"Wu, D., Wang, Y., Xia, S.T., et al.: Skip connections matter: on the transferability of adversarial examples generated with resnets. arXiv preprint arXiv: 2002.05990 (2020)","key":"7_CR15"},{"doi-asserted-by":"crossref","unstructured":"Drozdzal, M., Vorontsov, E., Chartrand, G., et al.: The importance of skip connections in biomedical image segmentation. In: International Workshop on Deep Learning in Med-ical Image Analysis, International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 179\u2013187 (2016)","key":"7_CR16","DOI":"10.1007\/978-3-319-46976-8_19"},{"doi-asserted-by":"crossref","unstructured":"Tustison, N., Gee, J.: N4ITK: Nick\u2019s N3 ITK implementation for MRI bias field correc-tion. Insight J. 1\u20138 (2009)","key":"7_CR17","DOI":"10.54294\/jculxw"},{"doi-asserted-by":"crossref","unstructured":"Tustison, N.J., Avants, B.B., Cook, P.A., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imag. 29(6), 1310\u20131320 (2010)","key":"7_CR18","DOI":"10.1109\/TMI.2010.2046908"},{"doi-asserted-by":"crossref","unstructured":"Mengqiao ,W., Jie, Y., Yilei, C., et al.: The multimodal brain tumor image segmentation based on convolutional neural networks. In: 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), pp. 336\u2013339. IEEE (2017)","key":"7_CR19","DOI":"10.1109\/CIAPP.2017.8167234"},{"doi-asserted-by":"crossref","unstructured":"Danielsson, P.E.: Euclidean distance mapping. Comput. Graph. Image Process. 14(3), 227\u2013248 (1980)","key":"7_CR20","DOI":"10.1016\/0146-664X(80)90054-4"},{"doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, Y., Feng, J.: On the Euclidean distance of images. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1334\u20131339 (2005)","key":"7_CR21","DOI":"10.1109\/TPAMI.2005.165"},{"doi-asserted-by":"crossref","unstructured":"Liberti, L., Lavor, C., Maculan, N., et al.: Euclidean distance geometry and applications. SIAM Rev. 56(1), 3\u201369 (2014)","key":"7_CR22","DOI":"10.1137\/120875909"},{"doi-asserted-by":"crossref","unstructured":"Pieper, S., Halle, M., Kikinis, R.: 3D slicer. In: 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821), pp. 632\u2013635. IEEE (2004)","key":"7_CR23","DOI":"10.1109\/ISBI.2004.1398617"},{"doi-asserted-by":"crossref","unstructured":"Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative Imaging and Im-age-Guided Therapy, pp. 277\u2013289 (2013)","key":"7_CR24","DOI":"10.1007\/978-1-4614-7657-3_19"},{"doi-asserted-by":"crossref","unstructured":"Qu, Y., Chen, Y., Huang, J., et al.: Enhanced pix2pix dehazing network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8160\u20138168 (2019)","key":"7_CR25","DOI":"10.1109\/CVPR.2019.00835"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., Yan, H., Huo, C., et al.: Enhancing Pix2Pix for remote sensing image classi-fication. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 2332\u20132336. IEEE (2018)","key":"7_CR26","DOI":"10.1109\/ICPR.2018.8545870"},{"doi-asserted-by":"crossref","unstructured":"Yang, H., Sun, J., Carass, A., et al.: Unpaired brain MR-to-CT synthesis using a struc-ture-constrained CycleGAN. In: Deep Learning in Medical Image Analysis and Multi-modal Learning for Clinical Decision Support (DLMIA), pp. 174\u2013182 (2018)","key":"7_CR27","DOI":"10.1007\/978-3-030-00889-5_20"},{"key":"7_CR28","first-page":"1","volume":"32","author":"M Loey","year":"2020","unstructured":"Loey, M., Manogaran, G., Khalifa, N.: A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural. Comput. Appl. 32, 1\u201313 (2020)","journal-title":"Neural. Comput. Appl."}],"container-title":["Communications in Computer and Information Science","International Conference on Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-5847-4_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T02:09:21Z","timestamp":1729994961000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-5847-4_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819958467","9789819958474"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-5847-4_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hefei","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dl2link.com\/ncaa2023\/","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":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","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":"83","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":"1","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":"39% - 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":"3.21","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":"3.67","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)"}}]}}