Name
Abstract | ||
|---|---|---|
Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registratio... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TMI.2021.3137280 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Strain,Training,Image segmentation,Brain,Image registration,National Institutes of Health,Deep learning | Journal | 41 |
Issue | ISSN | Citations |
5 | 0278-0062 | 0 |
PageRank | References | Authors |
0.34 | 0 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongming Wei | 1 | 0 | 0.34 |
| Sahar Ahmad | 2 | 12 | 8.33 |
| Yuyu Guo | 3 | 54 | 6.58 |
| Liyun Chen | 4 | 0 | 0.34 |
| Yunzhi Huang | 5 | 0 | 0.34 |
| Lei Ma | 6 | 0 | 2.03 |
| Zhengwang Wu | 7 | 60 | 16.97 |
| Gang Li | 8 | 298 | 51.27 |
| Li Wang | 9 | 1051 | 78.25 |
| Weili Lin | 10 | 156 | 32.78 |
| Pew-Thian Yap | 11 | 1093 | 93.77 |
| Dinggang Shen | 12 | 1 | 3.40 |
| Liyun Chen | 13 | 1 | 0.69 |