Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9561808 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
Keywords | DocType | Volume |
Computer Vision for Medical Robotics, Image Registration, Visual Tracking | Conference | 2021 |
Issue | ISSN | Citations |
1 | 1050-4729 | 0 |
PageRank | References | Authors |
0.34 | 16 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wentao Zhu | 1 | 250 | 19.35 |
| Yufang Huang | 2 | 3 | 1.38 |
| Daguang Xu | 3 | 50 | 14.28 |
| Zhen Qian | 4 | 133 | 20.23 |
| Wei Fan | 5 | 4205 | 253.58 |
| Xiaohui Xie | 6 | 61 | 5.50 |