{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:30:15Z","timestamp":1781713815111,"version":"3.54.5"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1007\/s11548-020-02141-y","type":"journal-article","created":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T11:02:37Z","timestamp":1587380557000},"page":"877-886","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Cardiac point-of-care to cart-based ultrasound translation using constrained CycleGAN"],"prefix":"10.1007","volume":"15","author":[{"given":"Mohammad H.","family":"Jafari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hany","family":"Girgis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan","family":"Van Woudenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathaniel","family":"Moulson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina","family":"Luong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Fung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shane","family":"Balthazaar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Jue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Micheal","family":"Tsang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parvathy","family":"Nair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ken","family":"Gin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Rohling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Purang","family":"Abolmaesumi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teresa","family":"Tsang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"issue":"8","key":"2141_CR1","first-page":"772","volume":"20","author":"A Achim","year":"2001","unstructured":"Achim A (2001) Novel bayesian multiscale method for speckle removal in medical ultrasound images. IEEE TMI 20(8):772\u2013783","journal-title":"IEEE TMI"},{"issue":"4","key":"2141_CR2","doi-asserted-by":"publisher","first-page":"R115","DOI":"10.1530\/ERP-18-0056","volume":"5","author":"M Alsharqi","year":"2018","unstructured":"Alsharqi M, Woodward W, Mumith J, Markham D, Upton R, Leeson P (2018) Artificial intelligence and echocardiography. Echo Res Pract 5(4):R115\u2013R125","journal-title":"Echo Res Pract"},{"key":"2141_CR3","unstructured":"Armanious K, Jiang C, Fischer M, K\u00fcstner T, Nikolaou K, Gatidis S, Yang B (2018) Medgan: medical image translation using GANs. arXiv preprint arXiv:1806.06397"},{"key":"2141_CR4","first-page":"696","volume-title":"Lecture Notes in Computer Science","author":"Delaram Behnami","year":"2019","unstructured":"Behnami D. Liao Z, Girgis H, Luong C, Rohling R, Gin K, Tsang T, Abolmaesumi P (2019) Dual-view joint estimation of left ventricular ejection fraction with uncertainty modelling in echocardiograms. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 696\u2013704"},{"issue":"3","key":"2141_CR5","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1109\/TIP.2011.2169273","volume":"21","author":"G Carneiro","year":"2012","unstructured":"Carneiro G, Nascimento JC, Freitas A (2012) The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Trans Image Process 21(3):968\u2013982","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"2141_CR6","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1364\/BOE.8.000679","volume":"8","author":"H Chen","year":"2017","unstructured":"Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, Wang G (2017) Low-dose CT via convolutional neural network. Biomed Opt Express 8(2):679\u2013694","journal-title":"Biomed Opt Express"},{"key":"2141_CR7","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/978-3-319-46723-8_56","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"Hao Chen","year":"2016","unstructured":"Chen H, Zheng Y, Park JH, Heng PA, Zhou SK (2016) Iterative multi-domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 487\u2013495"},{"key":"2141_CR8","doi-asserted-by":"crossref","unstructured":"Cherian A, Sullivan A (2019) Sem-GAN: Semantically-consistent image-to-image translation. In: 2019 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1797\u20131806","DOI":"10.1109\/WACV.2019.00196"},{"issue":"10","key":"2141_CR9","first-page":"2221","volume":"18","author":"P Coup\u00e9","year":"2009","unstructured":"Coup\u00e9 P (2009) Nonlocal means-based speckle filtering for ultrasound images. IEEE TIP 18(10):2221\u20132229","journal-title":"IEEE TIP"},{"key":"2141_CR10","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-030-00937-3_72","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Markus A. Degel","year":"2018","unstructured":"Degel MA, Navab N, Albarqouni S (2018) Domain and geometry agnostic CNNs for left atrium segmentation in 3D ultrasound. In: MICCAI, pp 630\u2013637"},{"key":"2141_CR11","doi-asserted-by":"crossref","unstructured":"Dietrichson F, Smistad E, Ostvik A, Lovstakken L (2018) Ultrasound speckle reduction using generative adversial networks. In: 2018 IEEE international ultrasonics symposium (IUS). IEEE, pp 1\u20134","DOI":"10.1109\/ULTSYM.2018.8579764"},{"key":"2141_CR12","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-00937-3_71","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Suyu Dong","year":"2018","unstructured":"Dong S, Luo G, Wang K, Cao S, Mercado A, Shmuilovich O, Zhang H, Li S (2018) Voxelatlasgan: 3D left ventricle segmentation on echocardiography with atlas guided generation and voxel-to-voxel discrimination. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 622\u2013629"},{"issue":"3","key":"2141_CR13","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.echo.2018.10.007","volume":"32","author":"JC Dykes","year":"2019","unstructured":"Dykes JC, Kipps AK, Chen A, Nourse S, Rosenthal DN, Tierney ESS (2019) Parental acquisition of echocardiographic images in pediatric heart transplant patients using a handheld device: a pilot telehealth study. J Am Soc Echocardiogr 32(3):404\u2013411","journal-title":"J Am Soc Echocardiogr"},{"issue":"1","key":"2141_CR14","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25(1):24\u201329","journal-title":"Nat Med"},{"issue":"6","key":"2141_CR15","doi-asserted-by":"publisher","first-page":"e329","DOI":"10.1097\/CCM.0000000000001620","volume":"44","author":"J Gaudet","year":"2016","unstructured":"Gaudet J, Waechter J, McLaughlin K, Ferland A, Godinez T, Bands C, Boucher P, Lockyer J (2016) Focused critical care echocardiography: development and evaluation of an image acquisition assessment tool. Crit Care Med 44(6):e329\u2013e335","journal-title":"Crit Care Med"},{"key":"2141_CR16","doi-asserted-by":"crossref","unstructured":"Goudarzi S, Asif A, Rivaz H (2019) Multi-focus ultrasound imaging using generative adversarial networks. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 1118\u20131121","DOI":"10.1109\/ISBI.2019.8759216"},{"key":"2141_CR17","doi-asserted-by":"crossref","unstructured":"Huang O, Long W, Bottenus N, Lerendegui M, Trahey GE, Farsiu S, Palmeri ML (2020) Mimicknet, mimicking clinical image post-processing under black-box constraints. IEEE Trans Med Imaging","DOI":"10.1109\/ULTSYM.2019.8925597"},{"key":"2141_CR18","doi-asserted-by":"crossref","unstructured":"Huo Y, Xu Z, Bao S, Assad A, Abramson RG, Landman BA (2018) Adversarial synthesis learning enables segmentation without target modality ground truth. In: IEEE ISBI, pp 1217\u20131220","DOI":"10.1109\/ISBI.2018.8363790"},{"key":"2141_CR19","doi-asserted-by":"crossref","unstructured":"Jafari MH, Girgis H, Abdi AH, Liao Z, Pesteie M, Rohling R, Gin K, Tsang T, Abolmaesumi P (2019) Semi-supervised learning for cardiac left ventricle segmentation using conditional deep generative models as prior. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, pp 649\u2013652","DOI":"10.1109\/ISBI.2019.8759292"},{"key":"2141_CR20","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/978-3-030-00889-5_4","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Mohammad H. Jafari","year":"2018","unstructured":"Jafari MH, Girgis H, Liao Z, Behnami D, Abdi A, Vaseli H, Luong C, Rohling R, Gin K, Tsang T (2018) A unified framework integrating recurrent fully-convolutional networks and optical flow for segmentation of the left ventricle in echocardiography data. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Berlin, pp 29\u201337"},{"issue":"6","key":"2141_CR21","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1007\/s11548-019-01954-w","volume":"14","author":"MH Jafari","year":"2019","unstructured":"Jafari MH, Girgis H, Van Woudenberg N, Liao Z, Rohling R, Gin K, Abolmaesumi P, Tsang T (2019) Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training. Int J Comput Assist Radiol Surg 14(6):1027\u20131037","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2141_CR22","first-page":"655","volume-title":"Lecture Notes in Computer Science","author":"Mohammad H. Jafari","year":"2019","unstructured":"Jafari MH, Liao Z, Girgis H, Pesteie M, Rohling R, Gin K, Tsang T, Abolmaesumi P (2019) Echocardiography segmentation by quality translation using anatomically constrained cyclegan. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 655\u2013663"},{"issue":"7","key":"2141_CR23","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1016\/j.echo.2018.01.014","volume":"31","author":"AM Johri","year":"2018","unstructured":"Johri AM, Durbin J, Newbigging J, Tanzola R, Chow R, De S, Tam J (2018) Cardiac point-of-care ultrasound: state-of-the-art in medical school education. J Am Soc Echocardiogr 31(7):749\u2013760","journal-title":"J Am Soc Echocardiogr"},{"issue":"3","key":"2141_CR24","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1093\/ehjci\/jev014","volume":"16","author":"RM Lang","year":"2015","unstructured":"Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T (2015) Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the american society of echocardiography and the european association of cardiovascular imaging. Eur Heart J-Cardiovasc Imaging 16(3):233\u2013271","journal-title":"Eur Heart J-Cardiovasc Imaging"},{"issue":"9","key":"2141_CR25","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","volume":"38","author":"S Leclerc","year":"2019","unstructured":"Leclerc S, Smistad E, Pedrosa J, \u00d8stvik A, Cervenansky F, Espinosa F, Espeland T, Berg EAR, Jodoin P, Grenier T, Lartizien C, D\u2019hooge J, Lovstakken L, Bernard O (2019) Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans Med Imaging 38(9):2198\u20132210","journal-title":"IEEE Trans Med Imaging"},{"issue":"7553","key":"2141_CR26","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"2141_CR27","first-page":"687","volume-title":"Lecture Notes in Computer Science","author":"Zhibin Liao","year":"2019","unstructured":"Liao Z, Jafari MH, Girgis H, Gin K, Rohling R, Abolmaesumi P, Tsang T (2019) Echocardiography view classification using quality transfer star generative adversarial networks. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 687\u2013695"},{"issue":"8","key":"2141_CR28","first-page":"1549","volume":"12","author":"G Litjens","year":"2019","unstructured":"Litjens G, Ciompi F, Wolterink JM, de Vos BD, Leiner T, Teuwen J, I\u0161gum I (2019) State-of-the-art deep learning in cardiovascular image analysis. JACC: Cardiovasc Imaging 12(8):1549\u20131565","journal-title":"JACC: Cardiovasc Imaging"},{"key":"2141_CR29","doi-asserted-by":"crossref","unstructured":"Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, Ni D, Wang T (2019) Deep learning in medical ultrasound analysis: a review. Engineering","DOI":"10.1016\/j.eng.2018.11.020"},{"key":"2141_CR30","unstructured":"Lyu Q, You C, Shan H, Wang G (2018) Super-resolution MRI through deep learning. arXiv preprint arXiv:1810.06776"},{"issue":"1","key":"2141_CR31","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/s41746-018-0065-x","volume":"1","author":"A Madani","year":"2018","unstructured":"Madani A, Ong JR, Tibrewal A, Mofrad MR (2018) Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ Digit Med 1(1):59","journal-title":"NPJ Digit Med"},{"issue":"4","key":"2141_CR32","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s12630-018-1049-7","volume":"65","author":"TJ McCormick","year":"2018","unstructured":"McCormick TJ, Miller EC, Chen R, Naik VN (2018) Acquiring and maintaining point-of-care ultrasound (POCUS) competence for anesthesiologists. Can J Anesth\/J Can d\u2019anesth\u00e9sie 65(4):427\u2013436","journal-title":"Can J Anesth\/J Can d\u2019anesth\u00e9sie"},{"issue":"8","key":"2141_CR33","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1109\/TMI.2006.877092","volume":"25","author":"JA Noble","year":"2006","unstructured":"Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25(8):987\u20131010. https:\/\/doi.org\/10.1109\/TMI.2006.877092","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"2141_CR34","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2018","unstructured":"Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook SA, de Marvao A, Dawes T, O\u2019Regan DP (2018) Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans Med Imaging 37(2):384\u2013395","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"2141_CR35","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.ultrasmedbio.2018.07.024","volume":"45","author":"A \u00d8stvik","year":"2019","unstructured":"\u00d8stvik A, Smistad E, Aase SA, Haugen BO, Lovstakken L (2019) Real-time standard view classification in transthoracic echocardiography using convolutional neural networks. Ultrasound Med Biol 45(2):374\u2013384","journal-title":"Ultrasound Med Biol"},{"key":"2141_CR36","doi-asserted-by":"crossref","unstructured":"Perdios D, Vonlanthen M, Besson A, Martinez F, Arditi M, Thiran JP (2018) Deep convolutional neural network for ultrasound image enhancement. In: 2018 IEEE international ultrasonics symposium (IUS). IEEE, pp 1\u20134","DOI":"10.1109\/ULTSYM.2018.8580183"},{"key":"2141_CR37","first-page":"234","volume-title":"Lecture Notes in Computer Science","author":"Olaf Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 234\u2013241"},{"issue":"2","key":"2141_CR38","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3390\/diagnostics9020061","volume":"9","author":"A Rykkje","year":"2019","unstructured":"Rykkje A, Carlsen JF, Nielsen MB (2019) Hand-held ultrasound devices compared with high-end ultrasound systems: a systematic review. Diagnostics 9(2):61","journal-title":"Diagnostics"},{"key":"2141_CR39","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"2141_CR40","doi-asserted-by":"crossref","unstructured":"Silva JF, Silva JM, Guerra A, Matos S, Costa C (2018) Ejection fraction classification in transthoracic echocardiography using a deep learning approach. In: 2018 IEEE 31st international symposium on computer-based medical systems (CBMS). IEEE, pp 123\u2013128","DOI":"10.1109\/CBMS.2018.00029"},{"key":"2141_CR41","unstructured":"Smistad E, \u00d8stvik A (2017) 2D left ventricle segmentation using deep learning. In: 2017 IEEE international ultrasonics symposium (IUS), IEEE, pp 1\u20134"},{"issue":"7","key":"2141_CR42","doi-asserted-by":"publisher","first-page":"72903","DOI":"10.1118\/1.4883815","volume":"41","author":"S Tsantis","year":"2014","unstructured":"Tsantis S (2014) Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction. Med Phys 41(7):72903","journal-title":"Med Phys"},{"key":"2141_CR43","unstructured":"Vedula S, Senouf O, Bronstein AM, Michailovich OV, Zibulevsky M (2017) Towards ct-quality ultrasound imaging using deep learning. arXiv preprint arXiv:1710.06304"},{"key":"2141_CR44","doi-asserted-by":"crossref","unstructured":"Veni G, Moradi M, Bulu H, Narayan G, Syeda-Mahmood T (2018) Echocardiography segmentation based on a shape-guided deformable model driven by a fully convolutional network prior. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp 898\u2013902","DOI":"10.1109\/ISBI.2018.8363716"},{"issue":"1","key":"2141_CR45","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.advms.2018.11.003","volume":"64","author":"P Wejner-Mik","year":"2019","unstructured":"Wejner-Mik P, Kasprzak JD, Filipiak-Strzecka D, Mi\u015bkowiec D, Lorens A, Lipiec P (2019) Personal mobile device-based pocket echocardiograph: the diagnostic value and clinical utility. Adv Med Sci 64(1):157\u2013161","journal-title":"Adv Med Sci"},{"key":"2141_CR46","doi-asserted-by":"publisher","unstructured":"Wejner-Mik P, Teneta A, Jankowski M, Czyszpak L, Wdowiak-Okrojek K, Szymczyk E, Kasprzak JD, Lipiec P (2019) Feasibility and clinical utility of real-time tele-echocardiography using personal mobile device-based pocket echocardiograph. Arch Med Sci. https:\/\/doi.org\/10.5114\/aoms.2019.83136","DOI":"10.5114\/aoms.2019.83136"},{"key":"2141_CR47","doi-asserted-by":"publisher","unstructured":"Wolterink JM (2019) Left ventricle segmentation in the era of deep learning. J Nucl Cardiol. https:\/\/doi.org\/10.1007\/s12350-019-01674-3","DOI":"10.1007\/s12350-019-01674-3"},{"key":"2141_CR48","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-00889-5_20","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Heran Yang","year":"2018","unstructured":"Yang H, Sun J, Carass A, Zhao C, Lee J, Xu Z, Prince J (2018) Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Berlin, pp 174\u2013182"},{"issue":"16","key":"2141_CR49","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","volume":"138","author":"J Zhang","year":"2018","unstructured":"Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, Lassen MH, Fan E, Aras MA, Jordan C (2018) Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation 138(16):1623\u20131635","journal-title":"Circulation"},{"key":"2141_CR50","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: IEEE CVPR","DOI":"10.1109\/CVPR.2018.00963"},{"key":"2141_CR51","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: IEEE CVPR, pp 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02141-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-020-02141-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02141-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T23:26:33Z","timestamp":1618874793000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-020-02141-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,20]]},"references-count":51,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,5]]}},"alternative-id":["2141"],"URL":"https:\/\/doi.org\/10.1007\/s11548-020-02141-y","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,20]]},"assertion":[{"value":"18 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2020","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":"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":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}