Abstract
We propose a trainable architecture for affine image registration to produce robust starting points for conventional image registration methods. Learning-based methods for image
registration often require networks with many parameters and heavily engineered cost functions and thus are complex and computationally expensive. Despite their success in recent
years, these methods often lack the accuracy of classical iterative image registration and
struggle with large deformations. On the other hand, iterative methods depend on good
initial estimates and tuned hyperparameters. We tackle this problem by combining effective shallow networks and classical optimization algorithms using strategies from the
field of meta-learning. The architecture presented in this work incorporates only firstorder gradient information of the given registration problems, making it highly flexible and
particularly well-suited as an initialization step for classical image registration.
registration often require networks with many parameters and heavily engineered cost functions and thus are complex and computationally expensive. Despite their success in recent
years, these methods often lack the accuracy of classical iterative image registration and
struggle with large deformations. On the other hand, iterative methods depend on good
initial estimates and tuned hyperparameters. We tackle this problem by combining effective shallow networks and classical optimization algorithms using strategies from the
field of meta-learning. The architecture presented in this work incorporates only firstorder gradient information of the given registration problems, making it highly flexible and
particularly well-suited as an initialization step for classical image registration.
| Original language | English |
|---|---|
| Title of host publication | Medical Imaging with Deep Learning |
| Publication date | 2022 |
| Publication status | Published - 2022 |
