{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T23:54:21Z","timestamp":1772841261899,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164392","type":"print"},{"value":"9783031164408","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16440-8_54","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"567-577","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["DeepRecon: Joint 2D Cardiac Segmentation and\u00a03D Volume Reconstruction via\u00a0a\u00a0Structure-Specific Generative Method"],"prefix":"10.1007","author":[{"given":"Qi","family":"Chang","sequence":"first","affiliation":[]},{"given":"Zhennan","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Mu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Di","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Khalid","family":"Sawalha","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Qilong","family":"Zhangli","sequence":"additional","affiliation":[]},{"given":"Mikael","family":"Kanski","sequence":"additional","affiliation":[]},{"given":"Subhi","family":"Al\u2019Aref","sequence":"additional","affiliation":[]},{"given":"Leon","family":"Axel","sequence":"additional","affiliation":[]},{"given":"Dimitris","family":"Metaxas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"3","key":"54_CR1","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","volume":"54","author":"BB Avants","year":"2011","unstructured":"Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3), 2033\u20132044 (2011)","journal-title":"Neuroimage"},{"key":"54_CR2","doi-asserted-by":"crossref","unstructured":"Awori, J., et al.: 3D models improve understanding of congenital heart disease. 3D Print. Med. 7(1), 1\u20139 (2021)","DOI":"10.1186\/s41205-021-00115-7"},{"key":"54_CR3","doi-asserted-by":"crossref","unstructured":"Biffi, C., et al.: 3D high-resolution cardiac segmentation reconstruction from 2D views using conditional variational autoencoders. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1643\u20131646. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759328"},{"issue":"12","key":"54_CR4","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1109\/TMI.2021.3090082","volume":"40","author":"VM Campello","year":"2021","unstructured":"Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M &MS challenge. IEEE Trans. Med. Imaging 40(12), 3543\u20133554 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"54_CR5","doi-asserted-by":"crossref","unstructured":"Chang, Q., Yan, Z., Lou, Y., Axel, L., Metaxas, D.N.: Soft-label guided semi-supervised learning for bi-ventricle segmentation in cardiac cine MRI. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1752\u20131755. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098546"},{"key":"54_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-030-93722-5_16","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge","author":"Q Chang","year":"2022","unstructured":"Chang, Q., et al.: An unsupervised 3D recurrent neural network for\u00a0slice misalignment correction in\u00a0cardiac MR imaging. In: Puyol Ant\u00f3n, E., et al. (eds.) STACOM 2021. LNCS, vol. 13131, pp. 141\u2013150. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-93722-5_16"},{"issue":"3","key":"54_CR7","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1109\/TMI.2007.907324","volume":"27","author":"DH Frakes","year":"2008","unstructured":"Frakes, D.H., et al.: A new method for registration-based medical image interpolation. IEEE Trans. Med. Imaging 27(3), 370\u2013377 (2008)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"54_CR8","unstructured":"Gao, Y., Zhou, M., Liu, D., Yan, Z., Zhang, S., Metaxas, D.: A data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark. arXiv preprint arXiv:2203.00131 (2022)"},{"key":"54_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-030-87199-4_6","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Gao","year":"2021","unstructured":"Gao, Y., Zhou, M., Metaxas, D.N.: UTNet: a hybrid transformer architecture for medical image segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 61\u201371. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_6"},{"key":"54_CR10","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"54_CR11","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"issue":"1","key":"54_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-70551-8","volume":"10","author":"T K\u00fcstner","year":"2020","unstructured":"K\u00fcstner, T., et al.: CINENET: deep learning-based 3d cardiac cine MRI reconstruction with multi-coil complex-valued 4d spatiotemporal convolutions. Sci. Rep. 10(1), 1\u201313 (2020)","journal-title":"Sci. Rep."},{"issue":"1","key":"54_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.camwa.2013.04.026","volume":"66","author":"J Leng","year":"2013","unstructured":"Leng, J., Xu, G., Zhang, Y.: Medical image interpolation based on multi-resolution registration. Comput. Math. Appl. 66(1), 1\u201318 (2013)","journal-title":"Comput. Math. Appl."},{"key":"54_CR14","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, J., Kreis, K., Torralba, A., Fidler, S.: Semantic segmentation with generative models: semi-supervised learning and strong out-of-domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8300\u20138311 (2021)","DOI":"10.1109\/CVPR46437.2021.00820"},{"key":"54_CR15","doi-asserted-by":"crossref","unstructured":"Liu, D., et al.: Transfusion: multi-view divergent fusion for medical image segmentation with transformers. arXiv preprint arXiv:2203.10726 (2022)","DOI":"10.1007\/978-3-031-16443-9_47"},{"key":"54_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/978-3-030-93722-5_34","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge","author":"D Liu","year":"2022","unstructured":"Liu, D., Yan, Z., Chang, Q., Axel, L., Metaxas, D.N.: Refined deep layer aggregation for\u00a0multi-disease, multi-view & multi-center cardiac MR segmentation. In: Puyol Ant\u00f3n, E., et al. (eds.) STACOM 2021. LNCS, vol. 13131, pp. 315\u2013322. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-93722-5_34"},{"issue":"12","key":"54_CR17","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.1109\/TPAMI.2010.46","volume":"32","author":"A Myronenko","year":"2010","unstructured":"Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262\u20132275 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"54_CR18","doi-asserted-by":"crossref","unstructured":"Petersen, S.E., et al.: UK biobank\u2019s cardiovascular magnetic resonance protocol. J. Cardiovascular Magnet. Reson. 18(1), 1\u20137 (2015)","DOI":"10.1186\/s12968-016-0227-4"},{"key":"54_CR19","doi-asserted-by":"crossref","unstructured":"Prakash, A., Powell, A.J., Geva, T.: Multimodality noninvasive imaging for assessment of congenital heart disease. Circul. Cardiovascular Imaging 3(1), 112\u2013125 (2010)","DOI":"10.1161\/CIRCIMAGING.109.875021"},{"key":"54_CR20","doi-asserted-by":"crossref","unstructured":"Richardson, E., et al.: Encoding in style: a stylegan encoder for image-to-image translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2287\u20132296 (2021)","DOI":"10.1109\/CVPR46437.2021.00232"},{"key":"54_CR21","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":"54_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"key":"54_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102037","volume":"71","author":"Y Xia","year":"2021","unstructured":"Xia, Y., Ravikumar, N., Greenwood, J.P., Neubauer, S., Petersen, S.E., Frangi, A.F.: Super-resolution of cardiac MR cine imaging using conditional GANs and unsupervised transfer learning. Med. Image Anal. 71, 102037 (2021)","journal-title":"Med. Image Anal."},{"key":"54_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/978-3-319-59448-4_46","volume-title":"Functional Imaging and Modelling of the Heart","author":"D Yang","year":"2017","unstructured":"Yang, D., Wu, P., Tan, C., Pohl, K.M., Axel, L., Metaxas, D.: 3D motion modeling and reconstruction of left ventricle wall in cardiac MRI. In: Pop, M., Wright, G.A. (eds.) FIMH 2017. LNCS, vol. 10263, pp. 481\u2013492. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59448-4_46"},{"key":"54_CR25","doi-asserted-by":"crossref","unstructured":"Ye, M., et al.: Deeptag: an unsupervised deep learning method for motion tracking on cardiac tagging magnetic resonance images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7261\u20137271 (2021)","DOI":"10.1109\/CVPR46437.2021.00718"},{"issue":"6","key":"54_CR26","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1016\/j.media.2014.03.002","volume":"18","author":"Y Yu","year":"2014","unstructured":"Yu, Y., Zhang, S., Li, K., Metaxas, D., Axel, L.: Deformable models with sparsity constraints for cardiac motion analysis. Med. Image Anal. 18(6), 927\u2013937 (2014)","journal-title":"Med. Image Anal."},{"key":"54_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"54_CR28","doi-asserted-by":"crossref","unstructured":"Zhangli, Q., et al.: Region proposal rectification towards robust instance segmentation of biological images. arXiv preprint arXiv:2203.02846 (2022)","DOI":"10.1007\/978-3-031-16440-8_13"},{"key":"54_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1007\/978-3-030-58520-4_35","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Shen, Y., Zhao, D., Zhou, B.: In-domain GAN inversion for real image editing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 592\u2013608. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_35"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16440-8_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T18:12:34Z","timestamp":1711563154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16440-8_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164392","9783031164408"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16440-8_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/en\/","order":11,"name":"conference_url","label":"Conference URL","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"3","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":"5","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)"}}]}}