Name
Abstract | ||
|---|---|---|
Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this issue and meanwhile retain the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using a variable splitting optimization scheme, ... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TMI.2021.3108881 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Iterative methods,Strain,Image registration,Deformable models,Optimization,Deep learning,Noise reduction | Journal | 41 |
Issue | ISSN | Citations |
1 | 0278-0062 | 0 |
PageRank | References | Authors |
0.34 | 0 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Jia | 1 | 19 | 4.97 |
| Alexander Thorley | 2 | 0 | 0.34 |
| Wei Chen | 3 | 9 | 2.82 |
| Huaqi Qiu | 4 | 1 | 1.03 |
| linlin shen | 5 | 153 | 23.71 |
| Iain B Styles | 6 | 0 | 0.34 |
| Hyung Jin Chang | 7 | 301 | 22.83 |
| Ales Leonardis | 8 | 1636 | 147.33 |
| Antonio de Marvao | 9 | 0 | 0.34 |
| Declan P. O'Regan | 10 | 0 | 0.34 |
| Daniel Rueckert | 11 | 0 | 0.34 |
| Jinming Duan | 12 | 130 | 19.92 |