A multi-scale unsupervised learning for deformable image registration.

Purpose: Image registration is a fundamental task in the area of image processing, and it is critical to many clinical applications, e.g., computer-assisted surgery. In this work, we attempt to design an effective framework that gains higher accuracy at a minimal cost of the invertibility of registration field.
Methods: A hierarchically aggregated transformation (HAT) module is proposed. Within each HAT module, we connect multiple convolutions in a hierarchical manner to capture the multi-scale context, enabling small and large displacements between a pair of images to be taken into account simultaneously during the registration process. Besides, an adaptive feature scaling (AFS) mechanism is presented to refine the multi-scale feature maps derived from the HAT module by rescaling channel-wise features in the global receptive field. Based on the HAT module and AFS mechanism, we establish an efficacious and efficient unsupervised deformable registration framework.
Results: The devised framework is validated on the dataset of SCARED and MICCAI Instrument Segmentation and Tracking Challenge 2015, and the experimental results demonstrate that our method achieves better registration accuracy with fewer number of folding pixels than three widely used baseline approaches of SyN, NiftyReg and VoxelMorph.
Conclusion: We develop a novel method for unsupervised deformable image registration by incorporating the HAT module and AFS mechanism into the framework, which provides a new way to obtain a desirable registration field between a pair of images.
(© 2021. CARS.)