{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:11:50Z","timestamp":1780614710146,"version":"3.54.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014535","name":"Center for Individualized Medicine, Mayo Clinic","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014535","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-00976-4","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T19:03:12Z","timestamp":1708110192000},"page":"1228-1238","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Generative Adversarial Networks for Brain MRI Synthesis: Impact of Training Set Size on Clinical Application"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8851-4518","authenticated-orcid":false,"given":"MM","family":"Zoghby","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-6095","authenticated-orcid":false,"given":"BJ","family":"Erickson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5926-7517","authenticated-orcid":false,"given":"GM","family":"Conte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"issue":"11","key":"976_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. 2020;63(11):139-144.","journal-title":"Commun ACM."},{"key":"976_CR2","unstructured":"Mirza M, Osindero S. Conditional Generative Adversarial Nets. arXiv [csLG]. Published online November 6, 2014. http:\/\/arxiv.org\/abs\/1411.1784"},{"key":"976_CR3","doi-asserted-by":"publisher","first-page":"164","DOI":"10.3389\/fpubh.2020.00164","volume":"8","author":"L Lan","year":"2020","unstructured":"Lan L, You L, Zhang Z, et al. Generative Adversarial Networks and Its Applications in Biomedical Informatics. Front Public Health. 2020;8:164.","journal-title":"Front Public Health."},{"issue":"10","key":"976_CR4","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):1993-2024.","journal-title":"IEEE Trans Med Imaging."},{"key":"976_CR5","unstructured":"Li HB, Conte GM, Anwar SM, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn). arXiv [eessIV]. Published online May 15, 2023. http:\/\/arxiv.org\/abs\/2305.09011"},{"key":"976_CR6","unstructured":"Kofler F, Meissen F, Steinbauer F, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting. arXiv [eessIV]. Published online May 15, 2023. http:\/\/arxiv.org\/abs\/2305.08992"},{"key":"976_CR7","doi-asserted-by":"publisher","unstructured":"Conte GM, Weston AD, Vogelsang DC, et al. Generative adversarial networks to synthesize missing T1 and FLAIR MRI sequences for use in a multisequence brain tumor segmentation model. Radiology. 2021;300(1):E319. https:\/\/doi.org\/10.1148\/radiol.2021203786","DOI":"10.1148\/radiol.2021203786"},{"key":"976_CR8","unstructured":"Baid U, Ghodasara S, Mohan S, et al. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. arXiv [csCV]. Published online July 5, 2021. http:\/\/arxiv.org\/abs\/2107.02314"},{"issue":"1","key":"976_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas S, Akbari H, Sotiras A, et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data. 2017;4(1):1-13.","journal-title":"Scientific Data."},{"key":"976_CR10","doi-asserted-by":"publisher","unstructured":"S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al. Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection (BraTS-TCGA-GBM). doi:https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q","DOI":"10.7937\/K9\/TCIA.2017.KLXWJJ1Q"},{"issue":"7825","key":"976_CR11","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ, et al. Array programming with NumPy. Nature. 2020;585(7825):357-362.","journal-title":"Nature."},{"key":"976_CR12","unstructured":"Brett M, Markiewicz CJ, Hanke M, et al. Nipy\/nibabel.; 2022. https:\/\/nipy.org\/nibabel\/"},{"key":"976_CR13","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. ; 2017:1125\u20131134.","DOI":"10.1109\/CVPR.2017.632"},{"key":"976_CR14","unstructured":"Biewald L. Experiment Tracking with Weights and Biases. Published online 2020. https:\/\/www.wandb.com\/"},{"key":"976_CR15","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. arXiv [csLG]. Published online January 2, 2012:2825\u20132830. Accessed June 1, 2023. https:\/\/www.jmlr.org\/papers\/volume12\/pedregosa11a\/pedregosa11a.pdf?ref=https:\/"},{"key":"976_CR16","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.453","volume":"2","author":"S van der Walt","year":"2014","unstructured":"van der Walt S, Sch\u00f6nberger JL, Nunez-Iglesias J, et al. scikit-image: image processing in Python. PeerJ. 2014;2:e453.","journal-title":"PeerJ."},{"issue":"1","key":"976_CR17","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/MSP.2008.930649","volume":"26","author":"Z Wang","year":"2009","unstructured":"Wang Z, Bovik AC. Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. IEEE Signal Process Mag. 2009;26(1):98-117.","journal-title":"IEEE Signal Process Mag."},{"issue":"4","key":"976_CR18","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600-612.","journal-title":"IEEE Trans Image Process."},{"issue":"5","key":"976_CR19","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/S1470-2045(19)30098-1","volume":"20","author":"P Kickingereder","year":"2019","unstructured":"Kickingereder P, Isensee F, Tursunova I, et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 2019;20(5):728-740.","journal-title":"Lancet Oncol."},{"key":"976_CR20","unstructured":"Isensee F, J\u00e4ger PF, Kohl SAA, Petersen J, Maier-Hein KH. Automated Design of Deep Learning Methods for Biomedical Image Segmentation. arXiv [csCV]. Published online April 17, 2019. http:\/\/arxiv.org\/abs\/1904.08128"},{"key":"976_CR21","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/RBME.2019.2946868","volume":"13","author":"M Ghaffari","year":"2020","unstructured":"Ghaffari M, Sowmya A, Oliver R. Automated Brain Tumor Segmentation Using Multimodal Brain Scans: A Survey Based on Models Submitted to the BraTS 2012\u20132018 Challenges. IEEE Rev Biomed Eng. 2020;13:156-168.","journal-title":"IEEE Rev Biomed Eng."},{"key":"976_CR22","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 \u2013 MICCAI 2015. Springer International Publishing; 2015:234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"976_CR23","doi-asserted-by":"crossref","unstructured":"Sun C, Shrivastava A, Singh S, Gupta A. Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision. ; 2017:843\u2013852.","DOI":"10.1109\/ICCV.2017.97"},{"key":"976_CR24","unstructured":"Hestness J, Narang S, Ardalani N, et al. Deep Learning Scaling is Predictable, Empirically. arXiv [csLG]. Published online December 1, 2017. http:\/\/arxiv.org\/abs\/1712.00409"},{"issue":"3","key":"976_CR25","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.zemedi.2021.11.006","volume":"32","author":"G Heilemann","year":"2022","unstructured":"Heilemann G, Matthewman M, Kuess P, et al. Can Generative Adversarial Networks help to overcome the limited data problem in segmentation? Z Med Phys. 2022;32(3):361-368.","journal-title":"Z Med Phys."},{"issue":"5","key":"976_CR26","doi-asserted-by":"publisher","first-page":"2157","DOI":"10.1002\/mp.13458","volume":"46","author":"X Dong","year":"2019","unstructured":"Dong X, Lei Y, Wang T, et al. Automatic multiorgan segmentation in thorax CT images using U-net-GAN. Med Phys. 2019;46(5):2157-2168.","journal-title":"Med Phys."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-00976-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-00976-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-00976-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T12:24:48Z","timestamp":1718195088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-00976-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["976"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-00976-4","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,16]]},"assertion":[{"value":"12 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This project was granted an exemption from the requirement for IRB approval (45 CFR 46.104d, Category 4).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}]}}