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
@article{arxiv.2103.13578,
title = {Test-Time Training for Deformable Multi-Scale Image Registration},
author = {Wentao Zhu and Yufang Huang and Daguang Xu and Zhen Qian and Wei Fan and Xiaohui Xie},
journal= {arXiv preprint arXiv:2103.13578},
year = {2021}
}