Uncertainty-based cardiac image registration using variational autoencoder with nonuniformly spaced control points.
Journal:
Computer methods and programs in biomedicine
Published Date:
Jun 23, 2025
Abstract
BACKGROUND AND OBJECTIVE: The Variational Bayesian (VB) image registration model has garnered recent attention for its ability to offer uncertainty, particularly in the context of cardiac motion estimation. Nonetheless, several challenges have plagued VB image registration. Firstly, the Convolutional Neural Networks (CNNs) in VB excel with grid-based image features make it challenging to extract features from non-uniformly located points located at tissue boundaries. Secondly, the underutilization of VB-provided uncertainty and the misfocus of the regions of interest (ROIs) lead to misleading generative likelihoods. Lastly, existing VB prior distributions struggle to balance the posterior-prior gap and reconstruction accuracy.
