Name
Title | ||
|---|---|---|
An efficient preconditioner for stochastic gradient descent optimization of image registration. |
Abstract | ||
|---|---|---|
Stochastic gradient descent (SGD) is commonly used to solve (parametric) image registration problems. In case of badly scaled problems, SGD however only exhibits sublinear convergence properties. In this paper we propose an efficient preconditioner estimation method to improve the convergence rate of SGD. Based on the observed distribution of voxel displacements in the registration, we estimate the diagonal entries of a preconditioning matrix, thus rescaling the optimization cost function. The preconditioner is efficient to compute and employ, and can be used for mono-modal as well as multi-modal cost functions, in combination with different transformation models like the rigid, affine and B-spline model. Experiments on different clinical data sets show that the proposed method indeed improves the convergence rate compared to SGD with speedups around 2~5 in all tested settings, while retaining the same level of registration accuracy. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2019.2897943 | IEEE transactions on medical imaging |
Keywords | Field | DocType |
Convergence,Stochastic processes,Cost function,Image registration,Jacobian matrices,Mathematical model | Convergence (routing),Affine transformation,Stochastic gradient descent,Mathematical optimization,Preconditioner,Stochastic process,Parametric statistics,Rate of convergence,Mathematics,Image registration | Journal |
Volume | Issue | ISSN |
38 | 10 | 1558-254X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchuan Qiao | 1 | 15 | 5.55 |
| B.P.F. Lelieveldt | 2 | 1331 | 115.59 |
| Marius Staring | 3 | 971 | 59.25 |