Test-Time Training for Deformable Multi-Scale Image Registration

• URL: http://arxiv.org/abs/2103.13578v1
• Date: Thu, 25 Mar 2021 03:22:59 GMT
• Title: Test-Time Training for Deformable Multi-Scale Image Registration
• Authors: Wentao Zhu and Yufang Huang and Daguang Xu and Zhen Qian and Wei Fan and Xiaohui Xie
• Abstract summary: Deep learning-based registration approaches such as VoxelMorph have been emerging and achieve competitive performance. We construct a test-time training for deep deformable image registration to improve the generalization ability of conventional learning-based registration model.
• Score: 15.523457398508263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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