Deformation Field Fusion for Medical Image Registration

Haifeng Zhao, Chi Zhang, Deyin Liu, Lin Yuanbo Wu

Abstract: Deformable medical image registration is to find a series of non-linear spatial transformations to align a pair of fixed and moving voxel images. Deep learning based registration models are effective in learning differences between such image pair to obtain the deformation field which is specialized in describing non-rigid deformations in the 3D voxel context. However, existing models tend to learn either one single deformation field only or multi-stage (multi-level) deformation fields progressively arriving at a final optimal field. Actually, deformation fields resulting from different architectures or losses are capable of capturing diverse types of deformations, complementing to each other. In this article, we propose a novel framework of fusing different deformation fields to acquire an overall field to describe all-round deformations, in which multiple complementary cues regarding deformable 3D voxels can be strategically leveraged to improve the alignment of the given image pair. The key to the effect of deformation field fusion for registration lies in two aspects: the fusion network architecture and the loss function. Thus, we develop a well-designed fusion block using ingenious operations based on different types of pooling, convolution, and concatenation. Moreover, since calculating the deformation field using a conventional similarity loss cannot describe the contextual variations which are inter-dependent in each pair of fixed and moving images, we propose a novel Contrast-Structural loss to enhance the motion displacement between the image pair by calculating the similarity of pixels in density values, while being ranged in their spatial proximity. Extensive experimental results demonstrate that our proposed method achieves state-of-the-art performance on currently mainstream benchmark datasets.