{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:19:46Z","timestamp":1742930386630,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030333904"},{"type":"electronic","value":"9783030333911"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33391-1_6","type":"book-chapter","created":{"date-parts":[[2019,10,13]],"date-time":"2019-10-13T01:52:23Z","timestamp":1570931543000},"page":"45-53","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Synthesising Images and Labels Between MR Sequence Types with CycleGAN"],"prefix":"10.1007","author":[{"given":"Eric","family":"Kerfoot","sequence":"first","affiliation":[]},{"given":"Esther","family":"Puyol-Ant\u00f3n","sequence":"additional","affiliation":[]},{"given":"Bram","family":"Ruijsink","sequence":"additional","affiliation":[]},{"given":"Rina","family":"Ariga","sequence":"additional","affiliation":[]},{"given":"Ernesto","family":"Zacur","sequence":"additional","affiliation":[]},{"given":"Pablo","family":"Lamata","sequence":"additional","affiliation":[]},{"given":"Julia","family":"Schnabel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,13]]},"reference":[{"issue":"11","key":"6_CR1","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., Lalande, A., Zotti, C., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR2","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"},{"issue":"1","key":"6_CR3","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1002\/mrm.24440","volume":"70","author":"L Feng","year":"2013","unstructured":"Feng, L., Srichai, M.B., Lim, R.P., et al.: Highly accelerated real-time cardiac cine MRI using k-t sparse-sense. Magn. Reson. Med. 70(1), 64\u201374 (2013)","journal-title":"Magn. Reson. Med."},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"6_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs\/1608.06993 (2016)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"4","key":"6_CR7","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1109\/TMI.2018.2876633","volume":"38","author":"Y Huo","year":"2018","unstructured":"Huo, Y., Xu, Z., Moon, H., et al.: Synseg-net: synthetic segmentation without target modality ground truth. IEEE Trans. Med. Imaging 38(4), 1016\u20131025 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"6_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/978-3-030-00946-5_4","volume-title":"Image Analysis for Moving Organ, Breast, and Thoracic Images","author":"E Kerfoot","year":"2018","unstructured":"Kerfoot, E., Puyol Anton, E., Ruijsink, B., Clough, J., King, A.P., Schnabel, J.A.: Automated CNN-based reconstruction of short-axis cardiac MR sequence from real-time image data. In: Stoyanov, D., et al. (eds.) RAMBO\/BIA\/TIA -2018. LNCS, vol. 11040, pp. 32\u201341. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00946-5_4"},{"key":"6_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/978-3-030-12029-0_40","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"E Kerfoot","year":"2019","unstructured":"Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A.: Left-ventricle quantification using residual U-net. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 371\u2013380. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_40"},{"key":"6_CR11","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"issue":"2","key":"6_CR12","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1161\/CIRCIMAGING.112.980037","volume":"6","author":"A La Gerche","year":"2013","unstructured":"La Gerche, A., Claessen, G., Van de Bruaene, A., et al.: Cardiac MRI: a new gold standard for ventricular volume quantification during high-intensity exercise. Circ. Cardiovasc. imaging 6(2), 329\u201338 (2013)","journal-title":"Circ. Cardiovasc. imaging"},{"issue":"5","key":"6_CR13","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1002\/jmri.21762","volume":"29","author":"P Lurz","year":"2009","unstructured":"Lurz, P., Muthurangu, V., Schievano, S., et al.: Feasibility and reproducibility of biventricular volumetric assessment of cardiac function during exercise using real-time radial k-t SENSE magnetic resonance imaging. J. Magn. Reson. Imaging 29(5), 1062\u20131070 (2009)","journal-title":"J. Magn. Reson. Imaging"},{"key":"6_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"6_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-319-67564-0_9","volume-title":"Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment","author":"B Ruijsink","year":"2017","unstructured":"Ruijsink, B., et al.: Semi-automatic cardiac and respiratory gated MRI for cardiac assessment during exercise. In: Cardoso, M.J., et al. (eds.) CMMI\/SWITCH\/RAMBO -2017. LNCS, vol. 10555, pp. 86\u201395. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67564-0_9"},{"issue":"3","key":"6_CR16","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1002\/1522-2586(200009)12:3<430::AID-JMRI8>3.0.CO;2-V","volume":"12","author":"RM Setser","year":"2000","unstructured":"Setser, R.M., Fischer, S.E., Lorenz, C.H.: Quantification of left ventricular function with magnetic resonance images acquired in real time. J. Magn. Reson. Imaging 12(3), 430\u2013438 (2000)","journal-title":"J. Magn. Reson. Imaging"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"6_CR18","unstructured":"Simard, P.Y., Steinkraus, D., Platt, J.C., et al.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR, vol. 3 (2003)"},{"key":"6_CR19","unstructured":"Welander, P., Karlsson, S., Eklund, A.: Generative adversarial networks for image-to-image translation on multi-contrast MR images-a comparison of cyclegan and unit. arXiv preprint arXiv:1806.07777 (2018)"},{"key":"6_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-319-68127-6_2","volume-title":"Simulation and Synthesis in Medical Imaging","author":"JM Wolterink","year":"2017","unstructured":"Wolterink, J.M., Dinkla, A.M., Savenije, M.H.F., Seevinck, P.R., van den Berg, C.A.T., I\u0161gum, I.: Deep MR to CT synthesis using unpaired data. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2017. LNCS, vol. 10557, pp. 14\u201323. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68127-6_2"},{"issue":"5","key":"6_CR21","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual u-net. IEEE Geosci. Remote Sens. Lett. 15(5), 749\u2013753 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33391-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:02:57Z","timestamp":1728777777000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33391-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030333904","9783030333911"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33391-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"13 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Domain Adaptation and Representation Transfer","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dart2019","order":10,"name":"conference_id","label":"Conference ID","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":"18","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":"12","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":"67% - 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":"2,9","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":"3","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}