Abstract
We introduce an end-to-end deep-learning framework
for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector
momentum-parameterized stationary velocity field (vSVF)
model. Specifically, it consists of three stages. In the first
stage, a multi-step affine network predicts affine transform
parameters. In the second stage, we use a U-Net-like network to generate a momentum, from which a velocity field
can be computed via smoothing. Finally, in the third stage,
we employ a self-iterable map-based vSVF component to
provide a non-parametric refinement based on the current
estimate of the transformation map. Once the model is
trained, a registration is completed in one forward pass. To
evaluate the performance, we conducted longitudinal and
cross-subject experiments on 3D magnetic resonance images (MRI) of the knee of the Osteoarthritis Initiative (OAI)
dataset. Results show that our framework achieves comparable performance to state-of-the-art medical image registration approaches, but it is much faster, with a better control of transformation regularity including the ability to produce approximately symmetric transformations, and combining affine as well as non-parametric registration.