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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Vydané v:	IEEE transactions on medical imaging Ročník 38; číslo 9; s. 2165 - 2176
Hlavní autori:	Krebs, Julian, Delingette, Herve, Mailhe, Boris, Ayache, Nicholas, Mansi, Tommaso
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Predmet:	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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Abstract	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.
AbstractList	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. We propose to learn a low-dimensional probabilistic deformation model from data which can be used for 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, 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 variational inference. In particular, we use a conditional variational autoencoder (CVAE) 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 diffusion-based filters. Additionally, 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 state-of-the-art performance with a mean DICE score of 81.2% and a mean Hausdorff distance of 7.3mm 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 0.32s. Besides, we visualized the learned latent space and show 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. 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.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.
Author	Krebs, Julian Delingette, Herve Mansi, Tommaso Ayache, Nicholas Mailhe, Boris
Author_xml	– sequence: 1 givenname: Julian orcidid: 0000-0002-3902-0223 surname: Krebs fullname: Krebs, Julian email: julian.krebs@inria.fr organization: Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, France – sequence: 2 givenname: Herve surname: Delingette fullname: Delingette, Herve organization: Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, France – sequence: 3 givenname: Boris surname: Mailhe fullname: Mailhe, Boris organization: Siemens Healthineers, Digital Services, Digital Technology and Innovation, Princeton, NJ, USA – sequence: 4 givenname: Nicholas surname: Ayache fullname: Ayache, Nicholas organization: Université Côte d'Azur, Inria, Epione Team, Sophia Antipolis, France – sequence: 5 givenname: Tommaso surname: Mansi fullname: Mansi, Tommaso organization: Siemens Healthineers, Digital Services, Digital Technology and Innovation, Princeton, NJ, USA
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Keywords	conditional variational autoencoder deep learning probabilistic encoding deformation transport latent variable model deformable registration
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Snippet	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... We propose to learn a low-dimensional probabilistic deformation model from data which can be used for registration and the analysis of deformations. The latent...
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SubjectTerms	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
Title	Learning a Probabilistic Model for Diffeomorphic Registration
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