DeepAI
Log In Sign Up

Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

11/03/2020
by   Julian Krebs, et al.
0

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.

READ FULL TEXT

page 1

page 3

page 8

page 10

07/31/2019

Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI

We propose to learn a probabilistic motion model from a sequence of imag...
12/18/2018

Learning a Probabilistic Model for Diffeomorphic Registration

We propose to learn a low-dimensional probabilistic deformation model fr...
01/18/2020

Accelerating the Registration of Image Sequences by Spatio-temporal Multilevel Strategies

Multilevel strategies are an integral part of many image registration al...
06/07/2021

Multi-frame sequence generator of 4D human body motion

We examine the problem of generating temporally and spatially dense 4D h...
03/24/2020

Registration by tracking for sequential 2D MRI

Our anatomy is in constant motion. With modern MR imaging it is possible...
01/05/2012

Probabilistic Motion Estimation Based on Temporal Coherence

We develop a theory for the temporal integration of visual motion motiva...