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Found 4 papers, 0 papers with code
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.
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI
We propose to learn a probabilistic motion model from a sequence of images.
Learning a Probabilistic Model for Diffeomorphic Registration
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.
Ranked #1 on
Diffeomorphic Medical Image Registration
on Automatic Cardiac Diagnosis Challenge (ACDC)
(using extra training data)
Deformable Medical Image Registration
Diffeomorphic Medical Image Registration
+1
Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space.
