Context-driven pyramid registration network for estimating large topology-preserved deformation

摘要

Deep learning-based deformable image registration methods have become attractive alternatives to tra-ditional methods because of their great performance and fast run time. However, it is still challenging for these methods to estimate large topology-preserved deformation, and the contextual information that is important for large deformation is also under-mined. To address these issues, we propose a novel unsupervised context-driven pyramid registration network for estimating large topology-preserved deformation named CPRNet. Specifically, based on the multi-resolution feature pyramids, we first design multi-receptive-field guidance modules, aiming at exploiting the multi-scale spatial correlation between features of two pyramids. Then we devise multi-view context fusion modules to dynamically fuse deep contextual information containing high-level semantic information from different views of feature maps. Further, we develop a residual estimation strategy to estimate the deformation in a coarse-to-fine man-ner. Moreover, a deformation field regularization module is proposed to address the challenge of balanc-ing the registration performance and topology preservation. The experiments both on liver computed tomography (CT) images and brain magnetic resonance (MR) images demonstrate that our proposed method provides effective and accurate registration on various datasets with a fast run time. Compared with existing learning-based registration methods, our proposed method exceeds the perfor-mance in most trials while maintaining desirable topology preservation capability and can potentially fit various image registration tasks. (c) 2022 Elsevier B.V. All rights reserved.