{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:21:42Z","timestamp":1743132102128,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031319747"},{"type":"electronic","value":"9783031319754"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-31975-4_25","type":"book-chapter","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T23:30:02Z","timestamp":1683761402000},"page":"326-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Latent-Space Disentanglement with\u00a0Untrained Generator Networks for\u00a0the\u00a0Isolation of\u00a0Different Motion Types in\u00a0Video Data"],"prefix":"10.1007","author":[{"given":"Abdullah","family":"Abdullah","sequence":"first","affiliation":[]},{"given":"Martin","family":"Holler","sequence":"additional","affiliation":[]},{"given":"Karl","family":"Kunisch","sequence":"additional","affiliation":[]},{"given":"Malena Sabate","family":"Landman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Abdullah, A., Holler, M., Kunisch, K., Landman, M.S.: Source code for: latent-space disentanglement with untrained generator networks for the isolation of different motion types in video data (2023). https:\/\/github.com\/hollerm\/generator_based_motion_isolation","DOI":"10.1007\/978-3-031-31975-4_25"},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3389\/fcvm.2020.00017","volume":"7","author":"A Bustin","year":"2020","unstructured":"Bustin, A., Fuin, N., Botnar, R.M., Prieto, C.: From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction. Front. Cardiovasc. Med. 7, 17 (2020). https:\/\/doi.org\/10.3389\/fcvm.2020.00017","journal-title":"Front. Cardiovasc. Med."},{"key":"25_CR3","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"25_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2015.02.008","volume":"134","author":"D Fortun","year":"2015","unstructured":"Fortun, D., Bouthemy, P., Kervrann, C.: Optical flow modeling and computation: a survey. Comput. Vis. Image Underst. 134, 1\u201321 (2015). https:\/\/doi.org\/10.1016\/j.cviu.2015.02.008","journal-title":"Comput. Vis. Image Underst."},{"key":"25_CR5","doi-asserted-by":"publisher","unstructured":"Fu, Y., Lei, Y., Wang, T., Curran, W.J., Liu, T., Yang, X.: Deep learning in medical image registration: a review. Phys. Med. Biol. 65(20), 20TR01 (2020). https:\/\/doi.org\/10.1088\/1361-6560\/ab843e","DOI":"10.1088\/1361-6560\/ab843e"},{"key":"25_CR6","unstructured":"H\u00e4lv\u00e4, H., et al.: Disentangling identifiable features from noisy data with structured nonlinear ICA. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"issue":"2","key":"25_CR7","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.media.2013.10.016","volume":"18","author":"V Hamy","year":"2014","unstructured":"Hamy, V., et al.: Respiratory motion correction in dynamic MRI using robust data decomposition registration - application to DCE-MRI. Med. Image Anal. 18(2), 301\u2013313 (2014). https:\/\/doi.org\/10.1016\/j.media.2013.10.016","journal-title":"Med. Image Anal."},{"key":"25_CR8","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/TSP.2020.2977256","volume":"68","author":"R Hyder","year":"2020","unstructured":"Hyder, R., Asif, M.S.: Generative models for low-dimensional video representation and reconstruction. IEEE Trans. Signal Process. 68, 1688\u20131701 (2020). https:\/\/doi.org\/10.1109\/TSP.2020.2977256","journal-title":"IEEE Trans. Signal Process."},{"issue":"3","key":"25_CR9","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/S0893-6080(98)00140-3","volume":"12","author":"A Hyv\u00e4rinen","year":"1999","unstructured":"Hyv\u00e4rinen, A., Pajunen, P.: Nonlinear independent component analysis: existence and uniqueness results. Neural Netw. 12(3), 429\u2013439 (1999). https:\/\/doi.org\/10.1016\/S0893-6080(98)00140-3","journal-title":"Neural Netw."},{"key":"25_CR10","unstructured":"Khemakhem, I., Kingma, D., Monti, R., Hyvarinen, A.: Variational autoencoders and nonlinear ICA: a unifying framework. In: International Conference on Artificial Intelligence and Statistics, pp. 2207\u20132217 (2020)"},{"issue":"1","key":"25_CR11","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TMI.2014.2343953","volume":"34","author":"SG Lingala","year":"2015","unstructured":"Lingala, S.G., DiBella, E., Jacob, M.: Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI. IEEE Trans. Med. Imaging 34(1), 72\u201385 (2015). https:\/\/doi.org\/10.1109\/TMI.2014.2343953","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"25_CR12","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1080\/10255842.2012.670855","volume":"17","author":"FP Oliveira","year":"2014","unstructured":"Oliveira, F.P., Tavares, J.M.R.: Medical image registration: a review. Comput. Methods Biomech. Biomed. Engin. 17(2), 73\u201393 (2014). https:\/\/doi.org\/10.1080\/10255842.2012.670855","journal-title":"Comput. Methods Biomech. Biomed. Engin."},{"key":"25_CR13","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: Advances in Neural information processing systems (2017)"},{"key":"25_CR14","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"25_CR15","doi-asserted-by":"publisher","first-page":"3654","DOI":"10.1118\/1.3160108","volume":"36","author":"A Rahmim","year":"2009","unstructured":"Rahmim, A., Tang, J., Zaidi, H.: Four-dimensional (4D) image reconstruction strategies in dynamic PET: beyond conventional independent frame reconstruction. Med. Phys. 36(8), 3654\u20133670 (2009). https:\/\/doi.org\/10.1118\/1.3160108","journal-title":"Med. Phys."},{"issue":"1","key":"25_CR16","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1002\/mrm.26352","volume":"78","author":"M Schloegl","year":"2017","unstructured":"Schloegl, M., Holler, M., Schwarzl, A., Bredies, K., Stollberger, R.: Infimal convolution of total generalized variation functionals for dynamic MRI. Magn. Reson. Med. 78(1), 142\u2013155 (2017). https:\/\/doi.org\/10.1002\/mrm.26352","journal-title":"Magn. Reson. Med."},{"key":"25_CR17","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.image.2018.12.002","volume":"72","author":"Z Tu","year":"2019","unstructured":"Tu, Z., et al.: A survey of variational and CNN-based optical flow techniques. Signal Process.: Image Commun. 72, 9\u201324 (2019). https:\/\/doi.org\/10.1016\/j.image.2018.12.002","journal-title":"Signal Process.: Image Commun."},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Tulyakov, S., Liu, M.Y., Yang, X., Kautz, J.: MoCoGAN: decomposing motion and content for video generation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1526\u20131535 (2018)","DOI":"10.1109\/CVPR.2018.00165"},{"issue":"7","key":"25_CR19","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1007\/s11263-020-01303-4","volume":"128","author":"D Ulyanov","year":"2020","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. Int. J. Comput. Vision 128(7), 1867\u20131888 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01303-4","journal-title":"Int. J. Comput. Vision"},{"issue":"5","key":"25_CR20","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1016\/j.media.2012.02.004","volume":"16","author":"G Wollny","year":"2012","unstructured":"Wollny, G., Kellman, P., Santos, A., Ledesma-Carbayo, M.J.: Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis. Med. Image Anal. 16(5), 1015\u20131028 (2012). https:\/\/doi.org\/10.1016\/j.media.2012.02.004","journal-title":"Med. Image Anal."},{"issue":"12","key":"25_CR21","doi-asserted-by":"publisher","first-page":"3337","DOI":"10.1109\/TMI.2021.3084288","volume":"40","author":"J Yoo","year":"2021","unstructured":"Yoo, J., Jin, K.H., Gupta, H., Yerly, J., Stuber, M., Unser, M.: Time-dependent deep image prior for dynamic MRI. IEEE Trans. Med. Imaging 40(12), 3337\u20133348 (2021). https:\/\/doi.org\/10.1109\/TMI.2021.3084288","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Scale Space and Variational Methods in Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31975-4_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:20:34Z","timestamp":1710246034000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31975-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031319747","9783031319754"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31975-4_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SSVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Scale Space and Variational Methods in Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santa Margherita di Pula","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scalespace2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eventi.unibo.it\/ssvm2023","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","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":"57","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":"79% - 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":"2","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)"}}]}}