  Diffeomorphic image registration, offering smooth transformation and topology
preservation, is required in many medical image analysis tasks.Traditional
methods impose certain modeling constraints on the space of admissible
transformations and use optimization to find the optimal transformation between
two images. Specifying the right space of admissible transformations is
challenging: the registration quality can be poor if the space is too
restrictive, while the optimization can be hard to solve if the space is too
general. Recent learning-based methods, utilizing deep neural networks to learn
the transformation directly, achieve fast inference, but face challenges in
accuracy due to the difficulties in capturing the small local deformations and
generalization ability. Here we propose a new optimization-based method named
DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which
utilizes deep neural network to model the space of admissible transformations.
A multilayer perceptron (MLP) with sinusoidal activation function is used to
represent the continuous velocity field and assigns a velocity vector to every
point in space, providing the flexibility of modeling complex deformations as
well as the convenience of optimization. Moreover, we propose a cascaded image
registration framework (Cas-DNVF) by combining the benefits of both
optimization and learning based methods, where a fully convolutional neural
network (FCN) is trained to predict the initial deformation, followed by DNVF
for further refinement. Experiments on two large-scale 3D MR brain scan
datasets demonstrate that our proposed methods significantly outperform the
state-of-the-art registration methods.
