1 research outputs found
To Learn or Not to Learn Features for Deformable Registration?
Feature-based registration has been popular with a variety of features
ranging from voxel intensity to Self-Similarity Context (SSC). In this paper,
we examine the question on how features learnt using various Deep Learning (DL)
frameworks can be used for deformable registration and whether this feature
learning is necessary or not. We investigate the use of features learned by
different DL methods in the current state-of-the-art discrete registration
framework and analyze its performance on 2 publicly available datasets. We draw
insights into the type of DL framework useful for feature learning and the
impact, if any, of the complexity of different DL models and brain parcellation
methods on the performance of discrete registration. Our results indicate that
the registration performance with DL features and SSC are comparable and stable
across datasets whereas this does not hold for low level features.Comment: 9 pages, 4 figure