ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration

TL;DR

This paper introduces ABN, a neural network designed for multi-stage deformable image registration that maintains image sharpness by combining short-term and long-term deformation learning, improving registration accuracy without blurring.

Contribution

The paper proposes a novel Anti-Blur Network (ABN) that effectively preserves image sharpness during multi-stage deformable registration, addressing a key limitation of existing methods.

Findings

ABN achieves accurate registration while maintaining image sharpness.

Extensive experiments on natural and medical images validate ABN's effectiveness.

ABN outperforms traditional multi-stage registration methods in preserving image quality.

Abstract

Deformable image registration, i.e., the task of aligning multiple images into one coordinate system by non-linear transformation, serves as an essential preprocessing step for neuroimaging data. Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the source image is repeatedly deformed in stages by a same neural network until it is well-aligned with the target image. Conventional methods for multi-stage registration can often blur the source image as the pixel/voxel values are repeatedly interpolated from the image generated by the previous stage. However, maintaining image quality such as sharpness during image registration is crucial to medical data analysis. In this paper, we study the problem of anti-blur deformable image registration and propose a novel solution, called Anti-Blur Network (ABN), for multi-stage image registration. Specifically, we use a pair of short-term registration and long-term memory networks to learn the nonlinear deformations at each stage, where the short-term registration network learns how to improve the registration accuracy incrementally and the long-term memory network combines all the previous deformations to allow an interpolation to perform on the raw image directly and preserve image sharpness. Extensive experiments on both natural and medical image datasets demonstrated that ABN can accurately register images while preserving their sharpness. Our code and data can be found at https://github.com/anonymous3214/ABN