{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:02:49Z","timestamp":1777489369580,"version":"3.51.4"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872304","type":"print"},{"value":"9783030872311","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87231-1_46","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T15:05:11Z","timestamp":1632323111000},"page":"471-481","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation"],"prefix":"10.1007","author":[{"given":"Hajar","family":"Emami","sequence":"first","affiliation":[]},{"given":"Ming","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Siamak P.","family":"Nejad-Davarani","sequence":"additional","affiliation":[]},{"given":"Carri K.","family":"Glide-Hurst","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"46_CR1","doi-asserted-by":"publisher","first-page":"101684","DOI":"10.1016\/j.compmedimag.2019.101684","volume":"79","author":"K Armanious","year":"2020","unstructured":"Armanious, K., et al.: MedGAN: medical image translation using GANs. Computerized Med. Imag. Graph. 79, 101684 (2020)","journal-title":"Computerized Med. Imag. Graph."},{"issue":"12","key":"46_CR2","doi-asserted-by":"publisher","first-page":"5659","DOI":"10.1002\/mp.13247","volume":"45","author":"S Chen","year":"2018","unstructured":"Chen, S., Qin, A., Zhou, D., Yan, D.: U-net-generated synthetic CT images for magnetic resonance imaging-only prostate intensity-modulated radiation therapy treatment planning. Med. Phys. 45(12), 5659\u20135665 (2018)","journal-title":"Med. Phys."},{"key":"46_CR3","doi-asserted-by":"crossref","unstructured":"Emami, H., Dong, M., Glide-Hurst, C.K.: Attention-guided generative adversarial network to address atypical anatomy in synthetic CT generation. In: 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pp. 188\u2013193. IEEE (2020)","DOI":"10.1109\/IRI49571.2020.00034"},{"issue":"8","key":"46_CR4","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1002\/mp.13047","volume":"45","author":"H Emami","year":"2018","unstructured":"Emami, H., Dong, M., Nejad-Davarani, S.P., Glide-Hurst, C.K.: Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Med. Phys. 45(8), 3627\u20133636 (2018)","journal-title":"Med. Phys."},{"key":"46_CR5","unstructured":"Emami, H., Liu, Q., Dong, M.: FREA-UNet: frequency-aware U-Net for modality transfer. arXiv preprint arXiv:2012.15397 (2020)"},{"issue":"9","key":"46_CR6","doi-asserted-by":"publisher","first-page":"3788","DOI":"10.1002\/mp.13672","volume":"46","author":"J Fu","year":"2019","unstructured":"Fu, J., et al.: Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging. Med. Phys. 46(9), 3788\u20133798 (2019)","journal-title":"Med. Phys."},{"key":"46_CR7","unstructured":"Fu, J., Yang, Y., Singhrao, K., Ruan, D., Low, D.A., Lewis, J.H.: Male pelvic synthetic CT generation from t1-weighted MRI using 2D and 3D convolutional neural networks. arXiv preprint arXiv:1803.00131 (2018)"},{"key":"46_CR8","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414\u20132423 (2016)","DOI":"10.1109\/CVPR.2016.265"},{"key":"46_CR9","doi-asserted-by":"crossref","unstructured":"Ge, Y., et al.: Unpaired MR to CT synthesis with explicit structural constrained adversarial learning. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1096\u20131099. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759529"},{"key":"46_CR10","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., Shlens, J.: Exploring the structure of a real-time, arbitrary neural artistic stylization network. arXiv preprint arXiv:1705.06830 (2017)","DOI":"10.5244\/C.31.114"},{"key":"46_CR11","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"46_CR12","doi-asserted-by":"crossref","unstructured":"Hamghalam, M., Lei, B., Wang, T.: High tissue contrast MRI synthesis using multi-stage attention-GAN for glioma segmentation. arXiv preprint arXiv:2006.05030 (2020)","DOI":"10.1609\/aaai.v34i04.5825"},{"issue":"4","key":"46_CR13","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1002\/mp.12155","volume":"44","author":"X Han","year":"2017","unstructured":"Han, X.: MR-based synthetic CT generation using a deep convolutional neural network method. Med. Phys. 44(4), 1408\u20131419 (2017)","journal-title":"Med. Phys."},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1501\u20131510 (2017)","DOI":"10.1109\/ICCV.2017.167"},{"issue":"1","key":"46_CR15","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TMI.2015.2461533","volume":"35","author":"T Huynh","year":"2015","unstructured":"Huynh, T., et al.: Estimating CT image from MRI data using structured random forest and auto-context model. IEEE Trans. Med. Imag. 35(1), 174\u2013183 (2015)","journal-title":"IEEE Trans. Med. Imag."},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"issue":"1","key":"46_CR17","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1186\/s13014-015-0549-7","volume":"10","author":"J Kim","year":"2015","unstructured":"Kim, J., et al.: Dosimetric evaluation of synthetic CT relative to bulk density assignment-based magnetic resonance-only approaches for prostate radiotherapy. Radiat. Oncol. 10(1), 239 (2015)","journal-title":"Radiat. Oncol."},{"issue":"8","key":"46_CR18","doi-asserted-by":"publisher","first-page":"3565","DOI":"10.1002\/mp.13617","volume":"46","author":"Y Lei","year":"2019","unstructured":"Lei, Y., et al.: MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. Med. Phys. 46(8), 3565\u20133581 (2019)","journal-title":"Med. Phys."},{"key":"46_CR19","doi-asserted-by":"crossref","unstructured":"Lin, J., Xia, Y., Qin, T., Chen, Z., Liu, T.Y.: Conditional image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5524\u20135532 (2018)","DOI":"10.1109\/CVPR.2018.00579"},{"key":"46_CR20","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794\u20132802 (2017)","DOI":"10.1109\/ICCV.2017.304"},{"issue":"18","key":"46_CR21","doi-asserted-by":"publisher","first-page":"185001","DOI":"10.1088\/1361-6560\/aada6d","volume":"63","author":"M Maspero","year":"2018","unstructured":"Maspero, M., et al.: Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys. Med. Biol. 63(18), 185001 (2018)","journal-title":"Phys. Med. Biol."},{"issue":"11","key":"46_CR22","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s00066-010-2105-6","volume":"186","author":"N Nakamura","year":"2010","unstructured":"Nakamura, N., et al.: Variability in bladder volumes of full bladders in definitive radiotherapy for cases of localized prostate cancer. Strahlentherapie und Onkologie 186(11), 637\u2013642 (2010)","journal-title":"Strahlentherapie und Onkologie"},{"key":"46_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/978-3-319-46976-8_18","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"D Nie","year":"2016","unstructured":"Nie, D., Cao, X., Gao, Y., Wang, L., Shen, D.: Estimating CT image from MRI data using 3D fully convolutional networks. In: Carneiro, G., et al. (eds.) LABELS\/DLMIA -2016. LNCS, vol. 10008, pp. 170\u2013178. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46976-8_18"},{"issue":"12","key":"46_CR24","doi-asserted-by":"publisher","first-page":"2720","DOI":"10.1109\/TBME.2018.2814538","volume":"65","author":"D Nie","year":"2018","unstructured":"Nie, D., et al.: Medical image synthesis with deep convolutional adversarial networks. IEEE Trans. Biomed. Eng. 65(12), 2720\u20132730 (2018)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"46_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/978-3-319-66179-7_48","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"D Nie","year":"2017","unstructured":"Nie, D., et al.: Medical image synthesis with context-aware generative adversarial networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 417\u2013425. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_48"},{"key":"46_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-030-00931-1_52","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Pan","year":"2018","unstructured":"Pan, Y., Liu, M., Lian, C., Zhou, T., Xia, Y., Shen, D.: Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer\u2019s disease diagnosis. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 455\u2013463. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_52"},{"issue":"4","key":"46_CR27","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1002\/mp.14062","volume":"47","author":"X Tie","year":"2020","unstructured":"Tie, X., Lam, S.K., Zhang, Y., Lee, K.H., Au, K.H., Cai, J.: Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients. Med. Phys. 47(4), 1750\u20131762 (2020)","journal-title":"Med. Phys."},{"key":"46_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-319-68127-6_2","volume-title":"Simulation and Synthesis in Medical Imaging","author":"JM Wolterink","year":"2017","unstructured":"Wolterink, J.M., Dinkla, A.M., Savenije, M.H.F., Seevinck, P.R., van den Berg, C.A.T., I\u0161gum, I.: Deep MR to CT synthesis using unpaired data. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2017. LNCS, vol. 10557, pp. 14\u201323. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68127-6_2"},{"issue":"7","key":"46_CR29","doi-asserted-by":"publisher","first-page":"2837","DOI":"10.1088\/0031-9155\/60\/7\/2837","volume":"60","author":"H Zhong","year":"2015","unstructured":"Zhong, H., Wen, N., Gordon, J.J., Elshaikh, M.A., Movsas, B., Chetty, I.J.: An adaptive MR-CT registration method for MRI-guided prostate cancer radiotherapy. Phys. Med. Biol. 60(7), 2837 (2015)","journal-title":"Phys. Med. Biol."},{"key":"46_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87231-1_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T20:06:10Z","timestamp":1698955570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87231-1_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872304","9783030872311"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87231-1_46","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":"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":"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":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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":"The conference 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)"}}]}}