Unsupervised deformable image registration with fully connected generative neural network

Ameneh Sheikhjafari, Michelle Noga, Kumaradevan Punithakumar, Nilanjan Ray

Abstract:

In this paper, a new deformable image registration method based on a fully connected
neural network is proposed. Even though a deformation field related to the
point correspondence between fixed and moving images are high-dimensional in
nature, we assume that these deformation fields form a low dimensional manifold
in many real world applications. Thus, in our method, a neural network generates
an embedding of the deformation field from a low dimensional vector. This low-dimensional
manifold formulation avoids the intractability associated with the high
dimensional search space that most other methods face during image registration.
As a result, while most methods rely on explicit and handcrafted regularization of
the deformation fields, our algorithm relies on implicitly regularizing the network
parameters. The proposed method generates deformation fields from latent low
dimensional space by minimizing a dissimilarity metric between a fixed image
and a warped moving image. Our method removes the need for a large dataset
to optimize the proposed network. The proposed method is quantitatively evaluated
using images from the MICCAI ACDC challenge. The results demonstrate
that the proposed method improves performance in comparison with a moving
mesh registration algorithm, and also it correlates well with independent manual
segmentations by an expert.

Author Affiliation: University of Alberta