Learning a Probabilistic Model for Diffeomorphic Registration

We propose to learn a low-dimensional probabilistic deformation model from data which can be used for the registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables to compare deformations, to generate norma...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 38; H. 9; S. 2165 - 2176
Hauptverfasser:	Krebs, Julian, Delingette, Herve, Mailhe, Boris, Ayache, Nicholas, Mansi, Tommaso
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithms Artificial Intelligence Computational modeling Computer Science Computer Vision and Pattern Recognition conditional variational autoencoder Deep Learning Deformable models Deformable registration Deformation deformation transport Estimation Forecasting Heart - diagnostic imaging Humans Image Processing, Computer-Assisted - methods Image registration latent variable model Machine Learning Magnetic Resonance Imaging, Cine Medical Imaging Metric space Models, Statistical Multiscale analysis probabilistic encoding Probabilistic logic Probabilistic models Registration Regularization Strain Training Transport Velocity distribution conditional variational autoencoder deep learning probabilistic encoding deformation transport latent variable model deformable registration
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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