{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T18:48:53Z","timestamp":1760986133118,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031185755"},{"type":"electronic","value":"9783031185762"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18576-2_10","type":"book-chapter","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T14:04:21Z","timestamp":1665151461000},"page":"97-105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["3D (c)GAN for\u00a0Whole Body MR Synthesis"],"prefix":"10.1007","author":[{"given":"Daniel","family":"Mensing","sequence":"first","affiliation":[]},{"given":"Jochen","family":"Hirsch","sequence":"additional","affiliation":[]},{"given":"Markus","family":"Wenzel","sequence":"additional","affiliation":[]},{"given":"Matthias","family":"G\u00fcnther","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"issue":"1","key":"10_CR1","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1148\/radiol.2015142272","volume":"277","author":"F Bamberg","year":"2015","unstructured":"Bamberg, F., et al.: Whole-body MR imaging in the German national cohort: rationale, design, and technical background. Radiology 277(1), 206\u2013220 (2015)","journal-title":"Radiology"},{"key":"10_CR2","doi-asserted-by":"publisher","unstructured":"Bergen, R.V., Rajotte, J.F., Yousefirizi, F., Klyuzhin, I.S., Rahmim, A., Ng, R.T.: 3D PET image generation with tumour masks using TGAN. In: Medical Imaging 2022: Image Processing, vol. 12032, p. 120321P (2022). https:\/\/doi.org\/10.1117\/12.2611292","DOI":"10.1117\/12.2611292"},{"key":"10_CR3","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. arXiv (2016)"},{"key":"10_CR4","unstructured":"Feng, R., Zhao, D., Zha, Z.: On noise injection in generative adversarial networks. arXiv (2020)"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"Granstedt, J.L., Kelkar, V.A., Zhou, W., Anastasio, M.A.: SlabGAN: a method for generating efficient 3D anisotropic medical volumes using generative adversarial networks. In: Medical Imaging 2021: Image Processing, vol. 11596, p. 1159617 (2021). https:\/\/doi.org\/10.1117\/12.2581380","DOI":"10.1117\/12.2581380"},{"key":"10_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. arXiv (2017)"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Hong, S., et al.: 3D-StyleGAN: a style-based generative adversarial network for generative modeling of three-dimensional medical images. arXiv (2021)","DOI":"10.1007\/978-3-030-88210-5_3"},{"key":"10_CR8","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. arXiv (2020)"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. arXiv (2018)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. arXiv (2019)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Kwon, G., Han, C., Kim, D.S.: Generation of 3D brain MRI using auto-encoding generative adversarial networks. arXiv (2019)","DOI":"10.1007\/978-3-030-32248-9_14"},{"key":"10_CR12","unstructured":"Lemay, A., Gros, C., Vincent, O., Liu, Y., Cohen, J.P., Cohen-Adad, J.: Benefits of linear conditioning with metadata for image segmentation. arXiv (2021)"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"Lim, J.H., Ye, J.C.: Geometric GAN (2017). https:\/\/doi.org\/10.48550\/ARXIV.1705.02894, https:\/\/arxiv.org\/abs\/1705.02894","DOI":"10.48550\/ARXIV.1705.02894"},{"key":"10_CR14","unstructured":"Liu, B., Zhu, Y., Song, K., Elgammal, A.: Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis. arXiv (2021)"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Perez, E., Strub, F., Vries, H.D., Dumoulin, V., Courville, A.: FiLM: visual reasoning with a general conditioning layer. arXiv (2017)","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"10_CR16","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv (2015)"},{"key":"10_CR17","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Instance normalization: The missing ingredient for fast stylization. CoRR abs\/1607.08022 (2016), http:\/\/arxiv.org\/abs\/1607.08022"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Volokitin, A., et al.: Modelling the distribution of 3D brain MRI using a 2D slice VAE. arXiv (2020)","DOI":"10.1007\/978-3-030-59728-3_64"},{"key":"10_CR19","unstructured":"Yaz\u0131c\u0131, Y., Foo, C.S., Winkler, S., Yap, K.H., Piliouras, G., Chandrasekhar, V.: The unusual effectiveness of averaging in GAN training. arXiv (2018)"}],"container-title":["Lecture Notes in Computer Science","Deep Generative Models"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18576-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T14:05:27Z","timestamp":1665151527000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18576-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031185755","9783031185762"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18576-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DGM4MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Deep Generative Models","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dgm4miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dgm4miccai.github.io\/","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":"15","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":"12","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":"80% - 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":"3","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)"}}]}}