Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images.

ABSTRACT: The aim of deformable brain image registration is to align anatomical structures, which can potentially vary with large and complex deformations. Anatomical structures vary in size and shape, requiring the registration algorithm to estimate deformation fields at various degrees of complexity. Here, we present a difficulty-aware model based on an attention mechanism to automatically identify hard-to-register regions, allowing better estimation of large complex deformations. The difficulty-aware model is incorporated into a cascaded neural network consisting of three sub-networks to fully leverage both global and local contextual information for effective registration. The first sub-network is trained at the image level to predict a coarse-scale deformation field, which is then used for init

SUBMITTER: Huang Y 

PROVIDER: S-EPMC7725910 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature