Name
Title | ||
|---|---|---|
Quantitative Error Prediction of Medical Image Registration using Regression Forests. |
Abstract | ||
|---|---|---|
•The proposed method enables automatic quality control of image registration.•Variations in results when fluctuating the initial state indicate uncertainty.•Intensity and registration-based features provide complementary information. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.media.2019.05.005 | Medical Image Analysis |
Keywords | Field | DocType |
Image registration,Registration accuracy,Uncertainty estimation,Regression forests | Computer vision,Regression,Pattern recognition,Ground truth,Artificial intelligence,Image registration,Mathematics | Journal |
Volume | ISSN | Citations |
56 | 1361-8415 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hessam Sokooti | 1 | 61 | 3.74 |
| Gorkem Saygili | 2 | 78 | 6.36 |
| Ben Glocker | 3 | 2157 | 119.81 |
| B.P.F. Lelieveldt | 4 | 1331 | 115.59 |
| Marius Staring | 5 | 971 | 59.25 |