{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:22:30Z","timestamp":1781605350498,"version":"3.54.5"},"publisher-location":"Cham","reference-count":21,"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_3","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T15:05:11Z","timestamp":1632323111000},"page":"24-33","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation"],"prefix":"10.1007","author":[{"given":"Junxiao","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789\u20138797 (2018)","DOI":"10.1109\/CVPR.2018.00916"},{"key":"3_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-32251-9_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"P Ernst","year":"2019","unstructured":"Ernst, P., Hille, G., Hansen, C., T\u00f6nnies, K., Rak, M.: A CNN-based framework for statistical assessment of spinal shape and curvature in whole-body MRI images of large populations. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 3\u201311. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_1"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Fu, C., et al.: Three dimensional fluorescence microscopy image synthesis and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2221\u20132229 (2018)","DOI":"10.1109\/CVPRW.2018.00298"},{"key":"3_CR4","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANS. In: Advances in Neural Information Processing Systems, pp. 5767\u20135777 (2017)"},{"key":"3_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/978-3-030-32239-7_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Gupta","year":"2019","unstructured":"Gupta, L., Klinkhammer, B.M., Boor, P., Merhof, D., Gadermayr, M.: GAN-based image enrichment in digital pathology boosts segmentation accuracy. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 631\u2013639. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_70"},{"key":"3_CR6","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANS trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems, pp. 6626\u20136637 (2017)"},{"key":"3_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-030-32248-9_18","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"PU Huang","year":"2019","unstructured":"Huang, P.U., et al.: CoCa-GAN: common-feature-learning-based context-aware generative adversarial network for glioma grading. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 155\u2013163. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_18"},{"issue":"2","key":"3_CR8","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: NNU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Meth. 18(2), 203\u2013211 (2021)","journal-title":"Nat. Meth."},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"3_CR10","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANS for improved quality, stability, and variation. In: International Conference on Learning Representations (2018)"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Kavur, A.E., et\u00a0al.: Chaos challenge-combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021)","DOI":"10.1016\/j.media.2020.101950"},{"key":"3_CR12","unstructured":"Kavur, A.E., Selver, M.A., Dicle, O., Bar\u0131s, M., Gezer, N.S.: Chaos-combined (CT-MR) healthy abdominal organ segmentation challenge data. In: Proceedings of IEEE International Symposium Biomedical Image (ISBI) (2019)"},{"key":"3_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (Poster) (2015)"},{"key":"3_CR14","unstructured":"Martin Arjovsky, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia (2017)"},{"key":"3_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Shen, L., et al.: Multi-domain image completion for random missing input data. IEEE Trans. Med. Imaging 40(4), 1113\u20131122 (2020)","DOI":"10.1109\/TMI.2020.3046444"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Xin, B., Hu, Y., Zheng, Y., Liao, H.: Multi-modality generative adversarial networks with tumor consistency loss for brain MR image synthesis. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1803\u20131807. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098449"},{"issue":"7","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1109\/TMI.2019.2895894","volume":"38","author":"B Yu","year":"2019","unstructured":"Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., Bourgeat, P.: EA-GANS: edge-aware generative adversarial networks for cross-modality MR image synthesis. IEEE Trans. Med. Imaging 38(7), 1750\u20131762 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yang, L., Zheng, Y.: Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9242\u20139251 (2018)","DOI":"10.1109\/CVPR.2018.00963"},{"key":"3_CR20","unstructured":"Zhu, D., et al.: UGAN: Untraceable GAN for multi-domain face translation. arXiv preprint arXiv:1907.11418 (2019)"},{"key":"3_CR21","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_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T20:02:15Z","timestamp":1698955335000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87231-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872304","9783030872311"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87231-1_3","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)"}}]}}