{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T05:50:32Z","timestamp":1772257832104,"version":"3.50.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597184","type":"print"},{"value":"9783030597191","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59719-1_49","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T14:02:56Z","timestamp":1601647376000},"page":"501-511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks"],"prefix":"10.1007","author":[{"given":"Hristina","family":"Uzunova","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Ehrhardt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heinz","family":"Handels","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"49_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1007\/11866565_113","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2006","author":"V Arsigny","year":"2006","unstructured":"Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-Euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924\u2013931. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11866565_113"},{"key":"49_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-030-32226-7_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Astaraki","year":"2019","unstructured":"Astaraki, M., Toma-Dasu, I., Smedby, \u00d6., Wang, C.: Normal appearance autoencoder for lung cancer detection and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 249\u2013256. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_28"},{"key":"49_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-030-00928-1_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"JP Cohen","year":"2018","unstructured":"Cohen, J.P., Luck, M., Honari, S.: Distribution matching losses can hallucinate features in medical image translation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 529\u2013536. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_60"},{"key":"49_CR4","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"49_CR5","series-title":"Informatik aktuell","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/978-3-662-46224-9_37","volume-title":"Bildverarbeitung f\u00fcr die Medizin 2015","author":"J Ehrhardt","year":"2015","unstructured":"Ehrhardt, J., Schmidt-Richberg, A., Werner, R., Handels, H.: Variational registration: a flexible open-source ITK toolbox for nonrigid image registration. In: Handels, H., Deserno, T.M., Meinzer, H.-P., Tolxdorff, T. (eds.) Bildverarbeitung f\u00fcr die Medizin 2015. I, pp. 209\u2013214. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-46224-9_37"},{"key":"49_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1007\/978-3-030-32245-8_87","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"I Ezhov","year":"2019","unstructured":"Ezhov, I., et al.: Neural parameters estimation for brain tumor growth modeling. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 787\u2013795. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_87"},{"issue":"1","key":"49_CR7","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neuroimage.2010.07.033","volume":"54","author":"V Fonov","year":"2011","unstructured":"Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L.: Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54(1), 313\u2013327 (2011)","journal-title":"NeuroImage"},{"key":"49_CR8","doi-asserted-by":"crossref","unstructured":"Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Synthetic data augmentation using GAN for improved liver lesion classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 289\u2013293 (2018)","DOI":"10.1109\/ISBI.2018.8363576"},{"key":"49_CR9","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27, pp. 2672\u20132680 (2014)"},{"issue":"4","key":"49_CR10","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1002\/hbm.10123","volume":"19","author":"A Hammers","year":"2003","unstructured":"Hammers, A., et al.: Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum. Brain Mapp. 19(4), 224\u2013247 (2003)","journal-title":"Hum. Brain Mapp."},{"key":"49_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1007\/978-3-540-75757-3_78","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2007","author":"C Hogea","year":"2007","unstructured":"Hogea, C., Davatzikos, C., Biros, G.: Modeling glioma growth and mass effect in 3D MR images of the brain. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4791, pp. 642\u2013650. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-75757-3_78"},{"key":"49_CR12","unstructured":"Kohl, S., et al.: A Probabilistic U-Net for segmentation of ambiguous images. In: Advances in Neural Information Processing Systems 31, pp. 6965\u20136975 (2018)"},{"issue":"3","key":"49_CR13","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1109\/TMI.2013.2293478","volume":"33","author":"D Kwon","year":"2014","unstructured":"Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K.M.: PORTR: pre-operative and post-recurrence brain tumor registration. IEEE Trans. Med. Imaging 33(3), 651\u2013667 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"49_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-030-32239-7_23","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Liu","year":"2019","unstructured":"Liu, J., Shen, C., Liu, T., Aguilera, N., Tam, J.: Active appearance model induced generative adversarial network for controlled data augmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 201\u2013208. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_23"},{"issue":"10","key":"49_CR15","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., Jakab, A., Bauer, S., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"49_CR16","doi-asserted-by":"crossref","unstructured":"Pfarrkirchner, B., Gsaxner, C., Schmalsteig, D., Egger, J., Lindner, L.: TuMore: generation of synthetic brain tumor MRI data for deep learning based segmentation approaches. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, p. 63 (2018)","DOI":"10.1117\/12.2315704"},{"key":"49_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-319-66182-7_31","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"M-M Roh\u00e9","year":"2017","unstructured":"Roh\u00e9, M.-M., Datar, M., Heimann, T., Sermesant, M., Pennec, X.: SVF-Net: learning deformable image registration using shape matching. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 266\u2013274. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_31"},{"issue":"3","key":"49_CR18","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.neuroimage.2007.09.031","volume":"39","author":"DW Shattuck","year":"2008","unstructured":"Shattuck, D.W., et al.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064\u20131080 (2008)","journal-title":"Neuroimage"},{"key":"49_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-00536-8_1","volume-title":"Simulation and Synthesis in Medical Imaging","author":"H-C Shin","year":"2018","unstructured":"Shin, H.-C., et al.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, A., Goksel, O., Oguz, I., Burgos, N. (eds.) SASHIMI 2018. LNCS, vol. 11037, pp. 1\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00536-8_1"},{"key":"49_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/978-3-030-32226-7_13","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Uzunova","year":"2019","unstructured":"Uzunova, H., Ehrhardt, J., Jacob, F., Frydrychowicz, A., Handels, H.: Multi-scale GANs for memory-efficient generation of high resolution medical images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 112\u2013120. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_13"},{"key":"49_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-3-319-66182-7_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"H Uzunova","year":"2017","unstructured":"Uzunova, H., Wilms, M., Handels, H., Ehrhardt, J.: Training CNNs for image registration from few samples with model-based data augmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 223\u2013231. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_26"},{"key":"49_CR22","doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8798\u20138807 (2018)","DOI":"10.1109\/CVPR.2018.00917"},{"key":"49_CR23","doi-asserted-by":"crossref","unstructured":"Wu, E., Wu, K., Cox, D., Lotter, W.: Conditional infilling GANs for data augmentation in mammogram classification. In: Image Analysis for Moving Organ, Breast, and Thoracic Images, pp. 98\u2013106 (2018)","DOI":"10.1007\/978-3-030-00946-5_11"},{"key":"49_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/978-3-030-32226-7_84","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Xing","year":"2019","unstructured":"Xing, Y., et al.: Adversarial pulmonary pathology translation for pairwise chest X-ray data augmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 757\u2013765. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_84"},{"key":"49_CR25","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neuroimage.2017.07.008","volume":"158","author":"X Yang","year":"2017","unstructured":"Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration \u2013 a deep learning approach. NeuroImage 158, 378\u2013396 (2017)","journal-title":"NeuroImage"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59719-1_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:07:46Z","timestamp":1759356466000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59719-1_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597184","9783030597191"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59719-1_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.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":"1809","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":"542","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":"30% - 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 due to the COVID-19 pandemic.","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)"}}]}}