  3D medical image registration is of great clinical importance. However,
supervised learning methods require a large amount of accurately annotated
corresponding control points (or morphing), which are very difficult to obtain.
Unsupervised learning methods ease the burden of manual annotation by
exploiting unlabeled data without supervision. In this paper, we propose a new
unsupervised learning method using convolutional neural networks under an
end-to-end framework, Volume Tweening Network (VTN), for 3D medical image
registration. We propose three innovative technical components: (1) An
end-to-end cascading scheme that resolves large displacement; (2) An efficient
integration of affine registration network; and (3) An additional invertibility
loss that encourages backward consistency. Experiments demonstrate that our
algorithm is 880x faster (or 3.3x faster without GPU acceleration) than
traditional optimization-based methods and achieves state-of-theart performance
in medical image registration.
