Dual-stream pyramid registration network.

Abstract

We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations.