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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2023.3243040
|2 doi
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|a eng
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| 100 |
1 |
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|a Ye, Meng
|e verfasserin
|4 aut
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| 245 |
1 |
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|a SequenceMorph
|b A Unified Unsupervised Learning Framework for Motion Tracking on Cardiac Image Sequences
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 03.07.2023
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|a Date Revised 03.01.2025
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Modern medical imaging techniques, such as ultrasound (US) and cardiac magnetic resonance (MR) imaging, have enabled the evaluation of myocardial deformation directly from an image sequence. While many traditional cardiac motion tracking methods have been developed for the automated estimation of the myocardial wall deformation, they are not widely used in clinical diagnosis, due to their lack of accuracy and efficiency. In this paper, we propose a novel deep learning-based fully unsupervised method, SequenceMorph, for in vivo motion tracking in cardiac image sequences. In our method, we introduce the concept of motion decomposition and recomposition. We first estimate the inter-frame (INF) motion field between any two consecutive frames, by a bi-directional generative diffeomorphic registration neural network. Using this result, we then estimate the Lagrangian motion field between the reference frame and any other frame, through a differentiable composition layer. Our framework can be extended to incorporate another registration network, to further reduce the accumulated errors introduced in the INF motion tracking step, and to refine the Lagrangian motion estimation. By utilizing temporal information to perform reasonable estimations of spatio-temporal motion fields, this novel method provides a useful solution for image sequence motion tracking. Our method has been applied to US (echocardiographic) and cardiac MR (untagged and tagged cine) image sequences; the results show that SequenceMorph is significantly superior to conventional motion tracking methods, in terms of the cardiac motion tracking accuracy and inference efficiency
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|a Journal Article
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| 700 |
1 |
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|a Yang, Dong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Qiaoying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kanski, Mikael
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Axel, Leon
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Metaxas, Dimitris N
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 8 vom: 03. Aug., Seite 10409-10426
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|x 1939-3539
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|g volume:45
|g year:2023
|g number:8
|g day:03
|g month:08
|g pages:10409-10426
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2023.3243040
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