  Deformable image registration is widely utilized in medical image analysis,
but most proposed methods fail in the situation of complex deformations. In
this paper, we pre-sent a cascaded feature warping network to perform the
coarse-to-fine registration. To achieve this, a shared-weights encoder network
is adopted to generate the feature pyramids for the unaligned images. The
feature warping registration module is then used to estimate the deformation
field at each level. The coarse-to-fine manner is implemented by cascading the
module from the bottom level to the top level. Furthermore, the multi-scale
loss is also introduced to boost the registration performance. We employ two
public benchmark datasets and conduct various experiments to evaluate our
method. The results show that our method outperforms the state-of-the-art
methods, which also demonstrates that the cascaded feature warping network can
perform the coarse-to-fine registration effectively and efficiently.
