Metric Learning for Image Registration
This addresses the need for more controlled and regular deformations in medical image analysis, offering a novel hybrid approach that combines deep learning with traditional optimization methods.
The paper tackles the problem of limited control over spatial regularity in deep learning-based image registration by learning a spatially-adaptive regularizer within an optimization-based model, enabling diffeomorphic transformations and preserving structural properties.
Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.