FocusMorph: A novel multi-scale fusion network for 3D brain MR image registration

Abstract

In the field of medical image processing, registration algorithms are crucial tools, especially in assisting physicians with aligning medical images acquired at different time points or through different modalities. These techniques are particularly important for medical applications such as disease diagnosis, lesion detection, surgical planning, and treatment monitoring. However, although most deep learning-based methods are capable of extracting multiscale features, they may fail to produce outputs that are directly related to the fnal deformation feld. Additionally, many methods based on the U-Net structure overly rely on the last layer of high-resolution images, which represents a significant drawback. To address these issues, we propose a novel unsupervised deformable registration method named FocusMorph. This method centers on the FLatten Transformer block and employs a focused linear attention mechanism to enhance attentional expressivity while maintaining low complexity. We have also designed a layer-by-layer output fusion mechanism and a motion image encoder specifically for medical image registration, which aids in continuously tracking positional differences between motion images and effectively fusing them. Experimental results indicate that the FocusMorph method surpasses current leading medical image registration techniques on two distinct brain image datasets. It achieves improvements in the Dice coefficient by 2.6% and 1.5%, respectively, confirming its superior performance and significant potential in image registration. These findings not only highlight FocusMorph’s robust registration capabilities but also underscore its promising prospects in medical image processing.