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Motion estimation of cardiac MRI videos is crucial for the evaluation of human heart anatomy and function.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
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Cardiac MRI: recent progress and continued challenges
James P Earls, Vincent B Ho, Thomas K Foo, Ernesto Castillo, and Scott D Flamm · 2002
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Learning a similarity metric discriminatively, with application to face verification
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Consistent estimation of cardiac motions by 4D image registration
Dinggang Shen, Hari Sundar, Zhong Xue, Yong Fan, and Harold Litt · 2005
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Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiography
Mathieu De Craene, Gemma Piella, Oscar Camara, Nicolas Duchateau, Etelvino Silva, Adelina Doltra, Jan D’hooge, Josep Brugada, Marta Sitges, and Alejandro F Frangi · 2012
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A comprehensive cardiac motion estimation framework using both untagged and 3-D tagged MR images based on nonrigid registration
Wenzhe Shi, Xiahai Zhuang, Haiyan Wang, Simon Duckett, Duy VN Luong, Catalina Tobon-Gomez, KaiPin Tung, Philip J Edwards, Kawal S Rhode, Reza S Razavi, et al · 2012
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Echocardiography and cardiac magnetic resonance-based feature tracking in the assessment of myocardial mechanics in tetralogy of fallot: an intermodality comparison
Asif Padiyath, Paul Gribben, Joseph R Abraham, Ling Li, Sheela Rangamani, Andreas Schuster, David A Danford, Gianni Pedrizzetti, and Shelby Kutty · 2013
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Benchmarking framework for myocardial tracking and deformation algorithms: An open access database
Catalina Tobon-Gomez, Mathieu De Craene, Kristin Mcleod, Lennart Tautz, Wenzhe Shi, Anja Hennemuth, Adityo Prakosa, Hengui Wang, Gerry Carr-White, Stam Kapetanakis, et al · 2013
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Data science bowl cardiac challenge data, 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
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Segflow: Joint learning for video object segmentation and optical flow
Jingchun Cheng, Yi-Hsuan Tsai, Shengjin Wang, and Ming-Hsuan Yang · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
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Victor Garcia and Joan Bruna · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Feature tracking cardiac magnetic resonance via deep learning and spline optimization
Davis M Vigneault, Weidi Xie, David A Bluemke, and J Alison Noble · 2017
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Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?
Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, et al · 2018
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Simon Meister, Junhwa Hur, and Stefan Roth · 2018
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Selflow: Self-supervised learning of optical flow
Pengpeng Liu, Michael Lyu, Irwin King, and Jia Xu · 2019
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Implementation and validation of a three-dimensional cardiac motion estimation network
Manuel A Morales, David Izquierdo-Garcia, Iman Aganj, Jayashree Kalpathy-Cramer, Bruce R Rosen, and Ciprian Catana · 2019
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Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, and Michael J. Black · 2019
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Prognostic implications of global longitudinal strain by feature-tracking cardiac magnetic resonance in st-elevation myocardial infarction
Martin Reindl, Christina Tiller, Magdalena Holzknecht, Ivan Lechner, Alexander Beck, David Plappert, Michelle Gorzala, Mathias Pamminger, Agnes Mayr, Gert Klug, et al · 2019
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Exploiting motion for deep learning reconstruction of extremely-undersampled dynamic MRI
Gavin Seegoolam, Jo Schlemper, Chen Qin, Anthony Price, Jo Hajnal, and Daniel Rueckert · 2019
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Fully automated myocardial strain estimation from cine MRI using convolutional neural networks
Esther Puyol-Antón, Bram Ruijsink, Wenjia Bai, Hélène Langet, Mathieu De Craene, Julia A Schnabel, Paolo Piro, Andrew P King, and Matthew Sinclair · 2018
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Joint learning of motion estimation and segmentation for cardiac MR image sequences
Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E Petersen, Stefan K Piechnik, Stefan Neubauer, and Daniel Rueckert · 2018
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Pwc-net: CNNs for optical flow using pyramid, warping, and cost volume
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Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation
Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, and Thomas S Huang · 2018
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Dynamic mri reconstruction with motion-guided network
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Global longitudinal strain analysis using cardiac MRI in aortic stenosis: Comparison with left ventricular remodeling, myocardial fibrosis, and 2-year clinical outcomes
Nicholas B Spath, Miquel Gomez, Russell J Everett, Scott Semple, Calvin WL Chin, Audrey C White, Alan G Japp, David E Newby, and Marc R Dweck · 2019
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Learning to self-train for semi-supervised few-shot classification
Qianru Sun, Xinzhe Li, Yaoyao Liu, Shibao Zheng, Tat-Seng Chua, and Bernt Schiele · 2019
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Temporal consistency objectives regularize the learning of disentangled representations
Gabriele Valvano, Agisilaos Chartsias, Andrea Leo, and Sotirios A Tsaftaris · 2019
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A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images
Liang Wang, Patrick Clarysse, Zhengjun Liu, Bin Gao, Wanyu Liu, Pierre Croisille, and Philippe Delachartre · 2019
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Unos: Unified unsupervised optical-flow and stereo-depth estimation by watching videos
Yang Wang, Peng Wang, Zhenheng Yang, Chenxu Luo, Yi Yang, and Wei Xu · 2019
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Yan Wu, Mihaela Rosca, and Timothy Lillicrap · 2019
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Tracking early reperfused myocardial infarction using cardiac MR
R Xia, T Zhu, Y Zhang, YS Chen, L Wang, JC Liao, YM Li, FJ Lü, and FB Gao · 2019
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A deep learning segmentation approach in free-breathing real-time cardiac magnetic resonance imaging
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A novel framework for 3D-2D vertebra matching
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Optical flow estimation using dual self-attention pyramid networks
Mingliang Zhai, Xuezhi Xiang, Rongfang Zhang, Ning Lv, and Abdulmotaleb El Saddik · 2019
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Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
Qiao Zheng, Hervé Delingette, and Nicholas Ayache · 2019
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Unsupervised event-based learning of optical flow, depth, and egomotion
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney, and Kostas Daniilidis · 2019
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