  Registration is a fundamental task in medical robotics and is often a crucial
step for many downstream tasks such as motion analysis, intra-operative
tracking and image segmentation. Popular registration methods such as ANTs and
NiftyReg optimize objective functions for each pair of images from scratch,
which are time-consuming for 3D and sequential images with complex
deformations. Recently, deep learning-based registration approaches such as
VoxelMorph have been emerging and achieve competitive performance. In this
work, we construct a test-time training for deep deformable image registration
to improve the generalization ability of conventional learning-based
registration model. We design multi-scale deep networks to consecutively model
the residual deformations, which is effective for high variational
deformations. Extensive experiments validate the effectiveness of multi-scale
deep registration with test-time training based on Dice coefficient for image
segmentation and mean square error (MSE), normalized local cross-correlation
(NLCC) for tissue dense tracking tasks. Two videos are in
https://www.youtube.com/watch?v=NvLrCaqCiAE and
https://www.youtube.com/watch?v=pEA6ZmtTNuQ
