Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion pa...

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Vydáno v:	IEEE transactions on medical imaging Ročník 40; číslo 5; s. 1405 - 1416
Hlavní autoři:	Krebs, Julian, Delingette, Herve, Ayache, Nicholas, Mansi, Tommaso
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Témata:	Algorithms Artificial Intelligence Computer Science Computer Vision and Pattern Recognition conditional variational autoencoder Data acquisition Data models Deformable models deformable registration Gaussian process Gaussian processes Humans Image Processing, Computer-Assisted Image sequences Interpolation latent variable model Machine Learning Magnetic Resonance Imaging Magnetic Resonance Imaging, Cine Medical Imaging Motion motion interpolation Motion model motion simulation Probabilistic logic Registration Simulation Strain Tracking motion simulation conditional variational autoencoder latent variable model motion model tracking deformable registration motion interpolation gaussian process
ISSN:	0278-0062, 1558-254X, 1558-254X
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Popis
Shrnutí:	We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0278-0062 1558-254X 1558-254X
DOI:	10.1109/TMI.2021.3056531