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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Vydáno v:IEEE transactions on medical imaging Ročník 38; číslo 9; s. 2165 - 2176
Hlavní autoři: Krebs, Julian, Delingette, Herve, Mailhe, Boris, Ayache, Nicholas, Mansi, Tommaso
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Témata:
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
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Popis
Shrnutí: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 normal or pathological deformations for any new image, or to transport deformations from one image pair to any other image. Our unsupervised method is based on the variational inference. In particular, we use a conditional variational autoencoder network and constrain transformations to be symmetric and diffeomorphic by applying a differentiable exponentiation layer with a symmetric loss function. We also present a formulation that includes spatial regularization such as the diffusion-based filters. In addition, our framework provides multi-scale velocity field estimations. We evaluated our method on 3-D intra-subject registration using 334 cardiac cine-MRIs. On this dataset, our method showed the state-of-the-art performance with a mean DICE score of 81.2% and a mean Hausdorff distance of 7.3 mm using 32 latent dimensions compared to three state-of-the-art methods while also demonstrating more regular deformation fields. The average time per registration was 0.32 s. Besides, we visualized the learned latent space and showed that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2019.2897112