{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:57:26Z","timestamp":1783972646232,"version":"3.55.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004489","name":"Mitacs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004489","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2019,10]]},"DOI":"10.1007\/s11548-019-02022-z","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T14:02:46Z","timestamp":1562594566000},"page":"1785-1794","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["CycleGAN for style transfer in X-ray angiography"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3109-6696","authenticated-orcid":false,"given":"Oleksandra","family":"Tmenova","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R\u00e9mi","family":"Martin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luc","family":"Duong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"2022_CR1","doi-asserted-by":"crossref","unstructured":"Chuquicusma MJ, Hussein S, Burt J, Bagci U (2018) How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp 240\u2013244). IEEE","DOI":"10.1109\/ISBI.2018.8363564"},{"key":"2022_CR2","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems (pp 2672\u20132680)"},{"key":"2022_CR3","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp 1125\u20131134)","DOI":"10.1109\/CVPR.2017.632"},{"key":"2022_CR4","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","volume":"35","author":"T Kooi","year":"2017","unstructured":"Kooi T, Litjens G, Van Ginneken B, Gubern-M\u00e9rida A, S\u00e1nchez CI, Mann R, den Heeten A, Karssemeijer N (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303\u2013312","journal-title":"Med Image Anal"},{"issue":"11","key":"2022_CR5","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","volume":"35","author":"P Liskowski","year":"2016","unstructured":"Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369\u20132380","journal-title":"IEEE Trans Med Imaging"},{"key":"2022_CR6","doi-asserted-by":"crossref","unstructured":"Martin R, Vachon E, Mir\u00f3 J, Duong L (2017) 3D reconstruction of vascular structures using graph-based voxel coloring. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017) (pp 1032-1035). IEEE","DOI":"10.1109\/ISBI.2017.7950692"},{"key":"2022_CR7","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.compbiomed.2016.10.001","volume":"79","author":"F M\u2019hiri","year":"2016","unstructured":"M\u2019hiri F, Duong L, Desrosiers C, Leye M, Mir\u00f3 J, Cheriet M (2016) A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences. Comput Biol Med 79:45\u201358","journal-title":"Comput Biol Med"},{"issue":"5","key":"2022_CR8","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240\u20131251","journal-title":"IEEE Trans Med Imaging"},{"key":"2022_CR9","unstructured":"Wang J, Perez L (2017) The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit"},{"key":"2022_CR10","doi-asserted-by":"crossref","unstructured":"Roth HR, Lee CT, Shin HC, Seff A, Kim L, Yao J, Lu L, Summers RM (2015) Anatomy-specific classification of medical images using deep convolutional nets. arXiv preprint \n                    arXiv:1504.04003","DOI":"10.1109\/ISBI.2015.7163826"},{"issue":"8","key":"2022_CR11","doi-asserted-by":"publisher","first-page":"3800","DOI":"10.1118\/1.2955743","volume":"35","author":"WP Segars","year":"2008","unstructured":"Segars WP, Mahesh M, Beck TJ, Frey EC, Tsui BM (2008) Realistic CT simulation using the 4D XCAT phantom. Med Phys 35(8):3800\u20133808","journal-title":"Med Phys"},{"key":"2022_CR12","doi-asserted-by":"crossref","unstructured":"Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W, Webb R (2017) Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp 2107\u20132116)","DOI":"10.1109\/CVPR.2017.241"},{"key":"2022_CR13","unstructured":"Vasconcelos CN, Vasconcelos BN (2017) Increasing deep learning melanoma classification by classical and expert knowledge based image transforms. CoRR. \n                    arXiv:1702.07025"},{"key":"2022_CR14","doi-asserted-by":"crossref","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision (pp 2223\u20132232)","DOI":"10.1109\/ICCV.2017.244"},{"key":"2022_CR15","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321\u2013331","journal-title":"Neurocomputing"},{"key":"2022_CR16","doi-asserted-by":"crossref","unstructured":"Bermudez C, Plassard AJ, Davis LT, Newton AT, Resnick SM, Landman BA (2018) Learning implicit brain MRI manifolds with deep learning. In: Medical imaging 2018: image processing (vol 10574, p 105741L). International Society for Optics and Photonics","DOI":"10.1117\/12.2293515"},{"key":"2022_CR17","unstructured":"Zhao H, Huiqi L, Li C (2017) Synthesizing filamentary structured images with gans. arXiv preprint. \n                    arXiv:1706.02185"},{"key":"2022_CR18","unstructured":"Guibas JT, Virdi TS, Li PS (2017) Synthetic medical images from dual generative adversarial networks. arXiv preprint. \n                    arXiv:1709.01872"},{"issue":"3","key":"2022_CR19","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1109\/TMI.2017.2759102","volume":"37","author":"P Costa","year":"2018","unstructured":"Costa P, Galdran A, Meyer MI, Niemeijer M, Abr\u00e0moff M, Mendon\u00e7a AM, Campilho A (2018) End-to-end adversarial retinal image synthesis. IEEE Trans Med Imaging 37(3):781\u2013791","journal-title":"IEEE Trans Med Imaging"},{"key":"2022_CR20","doi-asserted-by":"crossref","unstructured":"Appan P, Sivaswamy J (2018) Retinal image synthesis for cad development. In: International conference image analysis and recognition. Springer, Cham","DOI":"10.1007\/978-3-319-93000-8_70"},{"key":"2022_CR21","doi-asserted-by":"publisher","first-page":"53","DOI":"10.3389\/fbioe.2018.00053","volume":"6","author":"F Galbusera","year":"2018","unstructured":"Galbusera F, Niemeyer F, Seyfried M, Bassani T, Casaroli G, Kienle A, Wilke HJ (2018) Exploring the potential of generative adversarial networks for synthesizing radiological images of the spine to be used in in silico trials. Front Bioeng Biotechnol 6:53","journal-title":"Front Bioeng Biotechnol"},{"issue":"4","key":"2022_CR22","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal J, Abr\u00e0moff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501\u2013509","journal-title":"IEEE Trans Med Imaging"},{"key":"2022_CR23","unstructured":"VESsel SEgmentation in the Lung 2012, IEEE international symposium on biomedical imaging (ISBI 2012). \n                    https:\/\/vessel12.grand-challenge.org\/"},{"issue":"10","key":"2022_CR24","doi-asserted-by":"publisher","first-page":"2905","DOI":"10.1088\/0031-9155\/55\/10\/008","volume":"55","author":"C Rohkohl","year":"2010","unstructured":"Rohkohl C, Lauritsch G, Keil A, Hornegger J (2010) CAVAREV\u2014an open platform for evaluating 3D and 4D cardiac vasculature reconstruction. Phys Med Biol 55(10):2905","journal-title":"Phys Med Biol"},{"key":"2022_CR25","doi-asserted-by":"crossref","unstructured":"\u00c7imen S, Unberath M, Frangi A, Maier A (2017) CoronARe: a coronary artery reconstruction challenge. In: Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment (pp 96\u2013104). Springer, Cham","DOI":"10.1007\/978-3-319-67564-0_10"},{"issue":"2","key":"2022_CR26","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compmedimag.2013.07.001","volume":"38","author":"S Balocco","year":"2014","unstructured":"Balocco S, Gatta C, Ciompi F, Wahle A, Radeva P, Carlier S, Unal G, Sanidas E, Mauri J, Carillo X, Kovarnik T (2014) Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput Med Imaging Graph 38(2):70\u201390","journal-title":"Comput Med Imaging Graph"},{"key":"2022_CR27","doi-asserted-by":"crossref","unstructured":"Yan Z, Yang X, Tim Chen K-T (2018) A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J Biomed Health Inform","DOI":"10.1109\/JBHI.2018.2872813"},{"key":"2022_CR28","doi-asserted-by":"crossref","unstructured":"Lahiri A, Ayush K, Kumar Biswas P, Mitra P (2017) Generative adversarial learning for reducing manual annotation in semantic segmentation on large scale miscroscopy images: automated vessel segmentation in retinal fundus image as test case. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp 42\u201348)","DOI":"10.1109\/CVPRW.2017.110"},{"key":"2022_CR29","unstructured":"Tetteh G, Efremov V, Forkert ND, Schneider M, Kirschke J, Weber B, Zimmer C, Piraud M, Menze BH (2018) Deepvesselnet: vessel segmentation, centerline prediction, and bifurcation detection in 3-d angiographic volumes. arXiv preprint. \n                    arXiv:1803.09340"},{"issue":"7","key":"2022_CR30","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.2967\/jnumed.117.199414","volume":"59","author":"Hongyoon Choi","year":"2018","unstructured":"Choi Hongyoon, Lee Dong Soo (2018) Generation of structural MR images from amyloid PET: application to MR-less quantification. J Nucl Med 59(7):1111\u20131117","journal-title":"J Nucl Med"},{"key":"2022_CR31","doi-asserted-by":"crossref","unstructured":"Olut S, Sahin YH, Demir U, Unal G (2018) Generative adversarial training for MRA image synthesis using multi-contrast MRI. In: International workshop on predictive intelligence in medicine (pp 147\u2013154). Springer, Cham","DOI":"10.1007\/978-3-030-00320-3_18"},{"key":"2022_CR32","doi-asserted-by":"crossref","unstructured":"Bi L, Kim J, Kumar A, Feng D, Fulham M (2017) Synthesis of positron emission tomography (PET) images via multi-channel generative adversarial networks (GANs). In: molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment (pp 43\u201351). Springer, Cham","DOI":"10.1007\/978-3-319-67564-0_5"},{"key":"2022_CR33","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.engappai.2018.11.013","volume":"78","author":"A Ben-Cohen","year":"2019","unstructured":"Ben-Cohen A, Klang E, Raskin SP, Soffer S, Ben-Haim S, Konen E, Amitai MM, Greenspan H (2019) Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection. Eng Appl Artif Intell 78:186\u2013194","journal-title":"Eng Appl Artif Intell"},{"key":"2022_CR34","unstructured":"Liu M-Y, Breuel T, Kautz J (2017) Unsupervised image-to-image translation networks. Adv Neural Inf Process Syst"},{"key":"2022_CR35","unstructured":"Kim T, Cha M, Kim H, Lee JK, Kim J (2017) Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th international conference on machine learning (vol 70, pp 1857\u20131865). JMLR. org"},{"key":"2022_CR36","doi-asserted-by":"crossref","unstructured":"Chartsias A, Joyce T, Dharmakumar R, Tsaftaris SA (2017) Adversarial image synthesis for unpaired multi-modal cardiac data. In: International workshop on simulation and synthesis in medical imaging (pp 3\u201313). Springer, Cham","DOI":"10.1007\/978-3-319-68127-6_1"},{"key":"2022_CR37","doi-asserted-by":"crossref","unstructured":"Zhang Z, Yang L, Zheng Y (2018) 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","DOI":"10.1109\/CVPR.2018.00963"},{"key":"2022_CR38","doi-asserted-by":"crossref","unstructured":"Hiasa Y, Otake Y, Takao M, Matsuoka T, Takashima K, Carass A, Prince J, Sugano N, Sato Y (2018) Cross-modality image synthesis from unpaired data using CycleGAN. In: International workshop on simulation and synthesis in medical imaging (pp 31\u201341). Springer, Cham","DOI":"10.1007\/978-3-030-00536-8_4"},{"key":"2022_CR39","doi-asserted-by":"crossref","unstructured":"Wolterink JM, Dinkla AM, Savenije MH, Seevinck PR, van den Berg CA, I\u0161gum I (2017) Deep MR to CT synthesis using unpaired data. In: International workshop on simulation and synthesis in medical imaging (pp 14\u201323). Springer, Cham","DOI":"10.1007\/978-3-319-68127-6_2"},{"key":"2022_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2901750","author":"SU Dar","year":"2019","unstructured":"Dar SU, Yurt M, Karacan L, Erdem A, Erdem E, \u00c7ukur T (2019) Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans Med Imaging. \n                    https:\/\/doi.org\/10.1109\/TMI.2019.2901750","journal-title":"IEEE Trans Med Imaging"},{"key":"2022_CR41","unstructured":"Yang Q, Li N, Zhao Z, Fan X, Chang EC, Xu Y (2018) Mri image-to-image translation for cross-modality image registration and segmentation. arXiv preprint. \n                    arXiv:1801.06940"},{"key":"2022_CR42","unstructured":"Welander P, Karlsson S, Eklund A (2018) Generative adversarial networks for image-to-image translation on multi-contrast MR images\u2014a comparison of CycleGAN and UNIT. arXiv preprint. \n                    arXiv:1806.07777"},{"issue":"12","key":"2022_CR43","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TMI.2018.2842767","volume":"37","author":"Faisal Mahmood","year":"2018","unstructured":"Mahmood Faisal, Chen Richard, Durr Nicholas J (2018) Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Trans Med Imaging 37(12):2572\u20132581","journal-title":"IEEE Trans Med Imaging"},{"key":"2022_CR44","unstructured":"Yi X, Walia E, Babyn P (2018) Generative adversarial network in medical imaging: a review. arXiv preprint. \n                    arXiv:1809.07294"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-019-02022-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11548-019-02022-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-019-02022-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T23:20:25Z","timestamp":1594077625000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11548-019-02022-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,8]]},"references-count":44,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2019,10]]}},"alternative-id":["2022"],"URL":"https:\/\/doi.org\/10.1007\/s11548-019-02022-z","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,8]]},"assertion":[{"value":"17 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving Human Participants and\/or Animals"}},{"value":"Informed consent: Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}