Name
Abstract | ||
|---|---|---|
•The first investigation of correcting the intensity nonuniformity of infant brain MR images.•The first employment of a 3D adversarial framework to accurately predict the bias fields.•A novel local intensity uniformity loss to cope with the dynamic and heterogeneous intensity changes in infant brain MR images.•Extensive evaluations on a total of 1492 T1w and T2w MR images from neonates, infants, and adults show state-of-the-art performance and wide applicability of our method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.media.2021.102133 | Medical Image Analysis |
Keywords | DocType | Volume |
Intensity nonuniformity,Generative adversarial networks (GANs),Infant,MRI | Journal | 72 |
ISSN | Citations | PageRank |
1361-8415 | 0 | 0.34 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liangjun Chen | 1 | 1 | 2.04 |
| Zhengwang Wu | 2 | 60 | 16.97 |
| Dan Hu | 3 | 3 | 3.78 |
| fan wang | 4 | 34 | 18.08 |
| J Keith Smith | 5 | 79 | 7.32 |
| Weili Lin | 6 | 0 | 0.34 |
| Li Wang | 7 | 1051 | 78.25 |
| Dinggang Shen | 8 | 7837 | 611.27 |
| Gang Li | 9 | 386 | 27.90 |
| For Unc/Umn Baby Connectome Project Consortium | 10 | 0 | 0.34 |