{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T08:36:42Z","timestamp":1785314202136,"version":"3.55.0"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Generative adversarial networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they have been trained to replicate. One recurrent theme in medical imaging, is whether GANs can also be as effective at generating workable medical data, as they are for generating realistic RGB images. In this paper, we perform a multi-GAN and multi-application study, to gauge the benefits of GANs in medical imaging. We tested various GAN architectures, from basic DCGAN to more sophisticated style-based GANs, on three medical imaging modalities and organs, namely: cardiac cine-MRI, liver CT, and RGB retina images. GANs were trained on well-known and widely utilized datasets, from which their FID scores were computed, to measure the visual acuity of their generated images. We further tested their usefulness by measuring the segmentation accuracy of a U-Net trained on these generated images and the original data. The results reveal that GANs are far from being equal, as some are ill-suited for medical imaging applications, while others performed much better. The top-performing GANs are capable of generating realistic-looking medical images by FID standards, that can fool trained experts in a visual Turing test and comply to some metrics. However, segmentation results suggest that no GAN is capable of reproducing the full richness of medical datasets.<\/jats:p>","DOI":"10.3390\/jimaging9030069","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T04:10:48Z","timestamp":1679026248000},"page":"69","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":243,"title":["GANs for Medical Image Synthesis: An Empirical Study"],"prefix":"10.3390","volume":"9","author":[{"given":"Youssef","family":"Skandarani","sequence":"first","affiliation":[{"name":"ImViA Laboratory, University of Bourgogne Franche-Comte, 21000 Dijon, France"},{"name":"CASIS Inc., 21800 Quetigny, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre-Marc","family":"Jodoin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Sherbrooke, Sherbrooke, QC J1K 2R1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-366X","authenticated-orcid":false,"given":"Alain","family":"Lalande","sequence":"additional","affiliation":[{"name":"ImViA Laboratory, University of Bourgogne Franche-Comte, 21000 Dijon, France"},{"name":"Department of Medical Imaging, University Hospital of Dijon, 21079 Dijon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"ref_1","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u20131). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_2","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2019, January 6\u20139). Large Scale GAN Training for High Fidelity Natural Image Synthesis. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10278-021-00556-w","article-title":"Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation","volume":"35","author":"Jeong","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1148\/rg.2021200151","article-title":"Generative Adversarial Networks: A Primer for Radiologists","volume":"41","author":"Wolterink","year":"2021","journal-title":"Radiographics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.2174\/1381612826666201125110710","article-title":"Generative Adversarial Networks in Medical Image Processing","volume":"27","author":"Gong","year":"2020","journal-title":"Curr. Pharm. Des."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1109\/TMI.2018.2800298","article-title":"Simulation and Synthesis in Medical Imaging","volume":"37","author":"Frangi","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","first-page":"105741L","article-title":"Learning implicit brain MRI manifolds with deep learning","volume":"Volume 10574","author":"Bermudez","year":"2018","journal-title":"Medical Imaging 2018: Image Processing"},{"key":"ref_10","unstructured":"Baur, C., Albarqouni, S., and Navab, N. (2018). OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, Springer International Publishing."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Calimeri, F., Marzullo, A., Stamile, C., and Terracina, G. (2017, January 6\u20139). Biomedical data augmentation using generative adversarial neural networks. Proceedings of the International Conference on Artificial Neural Networks, Bristol, UK.","DOI":"10.1007\/978-3-319-68612-7_71"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chuquicusma, M.J.M., Hussein, S., Burt, J., and Bagci, U. (2018, January 4\u20137). How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363564"},{"key":"ref_13","unstructured":"Shin, H.C., Tenenholtz, N.A., Rogers, J.K., Schwarz, C.G., Senjem, M.L., Gunter, J.L., Andriole, K.P., and Michalski, M. (2018). Simulation and Synthesis in Medical Imaging, Springer International Publishing."},{"key":"ref_14","unstructured":"Skandarani, Y., Painchaud, N., Jodoin, P.M., and Lalande, A. (2020, January 6\u20139). On the effectiveness of GAN generated cardiac MRIs for segmentation. Proceedings of the Medical Imaging with Deep Learning, Montreal, QC, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101938","DOI":"10.1016\/j.artmed.2020.101938","article-title":"GANs for medical image analysis","volume":"109","author":"Kazeminia","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gonog, L., and Zhou, Y. (2019, January 19\u201321). A Review: Generative Adversarial Networks. Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi\u2019an, China.","DOI":"10.1109\/ICIEA.2019.8833686"},{"key":"ref_17","unstructured":"Arjovsky, M., and Bottou, L. (2017, January 24\u201326). Towards Principled Methods for Training Generative Adversarial Networks. Proceedings of the 5th International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_18","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016, January 2\u20134). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Proceedings of the 4th International Conference on Learning Representations (ICLR), Juan, Puerto Rico."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., and Paul Smolley, S. (2017, January 22\u201329). Least Squares Generative Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_20","first-page":"214","article-title":"Wasserstein Generative Adversarial Networks","volume":"Volume 70","author":"Arjovsky","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref_21","unstructured":"Lim, J.H., and Ye, J.C. (2017). Geometric GAN. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., and Zhu, J.Y. (2019, January 15\u201320). Semantic Image Synthesis with Spatially-Adaptive Normalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00244"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-To-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 13\u201319). Analyzing and Improving the Image Quality of StyleGAN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_25","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (May, January 30). Progressive Growing of GANs for Improved Quality, Stability, and Variation. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Regmi, K., and Borji, A. (2018, January 18\u201322). Cross-View Image Synthesis Using Conditional GANs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00369"},{"key":"ref_27","first-page":"2642","article-title":"Conditional Image Synthesis with Auxiliary Classifier GANs","volume":"Volume 70","author":"Odena","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201322). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_29","unstructured":"Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., and Garnett, R. (2016). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_30","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.cviu.2018.10.009","article-title":"Pros and cons of GAN evaluation measures","volume":"179","author":"Borji","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_32","unstructured":"Bi\u0144kowski, M., Sutherland, D.J., Arbel, M., and Gretton, A. (May, January 30). Demystifying MMD GANs. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_33","unstructured":"Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (2018). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_34","first-page":"7559","article-title":"Differentiable Augmentation for Data-Efficient GAN Training","volume":"Volume 33","author":"Larochelle","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_35","unstructured":"Ioffe, S., and Szegedy, C. (2015;, January 7\u20139). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2017, January 21\u201326). Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.437"},{"key":"ref_37","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Shmelkov, K., Schmid, C., and Alahari, K. (2018, January 8\u201314). How good is my GAN?. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01216-8_14"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_40","first-page":"1","article-title":"3D Segmentation in the Clinic: A Grand Challenge II: MS lesion segmentation","volume":"2008","author":"Styner","year":"2008","journal-title":"MIDAS J."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., and M\u00e9riaudeau, F. (2018). Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research. Data, 3.","DOI":"10.3390\/data3030025"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"UMAP: Uniform Manifold Approximation and Projection","volume":"3","author":"McInnes","year":"2018","journal-title":"J. Open Source Softw."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/3\/69\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:56:50Z","timestamp":1760122610000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/3\/69"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,16]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["jimaging9030069"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9030069","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,16]]}}}