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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Bibliographic Details
Published in:	IEEE transactions on medical imaging Vol. 40; no. 5; pp. 1405 - 1416
Main Authors:	Krebs, Julian, Delingette, Herve, Ayache, Nicholas, Mansi, Tommaso
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	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
Online Access:	Get full text
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