Learning a Probabilistic Model for Diffeomorphic Registration

• Published in: IEEE transactions on medical imaging Vol. 38; no. 9; pp. 2165 - 2176
• Main Authors: Krebs, Julian, Delingette, Herve, Mailhe, Boris, Ayache, Nicholas, Mansi, Tommaso
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: 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

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...