MsMorph: An Unsupervised pyramid learning network for brain image registration

TL;DR

MsMorph is an unsupervised deep learning framework for brain image registration that improves accuracy and interpretability by mimicking manual registration processes and leveraging multi-scale feature differences.

Contribution

It introduces a novel pyramid learning network that enhances registration accuracy and interpretability by simulating manual registration and using multi-scale feature differences.

Findings

Outperforms existing methods on brain MRI datasets

Achieves higher Dice scores and better surface distance metrics

Provides interpretable deformation fields

Abstract

In the field of medical image analysis, image registration is a crucial technique. Despite the numerous registration models that have been proposed, existing methods still fall short in terms of accuracy and interpretability. In this paper, we present MsMorph, a deep learning-based image registration framework aimed at mimicking the manual process of registering image pairs to achieve more similar deformations, where the registered image pairs exhibit consistency or similarity in features. By extracting the feature differences between image pairs across various as-pects using gradients, the framework decodes semantic information at different scales and continuously compen-sates for the predicted deformation field, driving the optimization of parameters to significantly improve registration accuracy. The proposed method simulates the manual approach to registration, focusing on different regions of the image pairs and their neighborhoods to predict the deformation field between the two images, which provides strong interpretability. We compared several existing registration methods on two public brain MRI datasets, including LPBA and Mindboggle. The experimental results show that our method consistently outperforms state of the art in terms of metrics such as Dice score, Hausdorff distance, average symmetric surface distance, and non-Jacobian. The source code is publicly available at https://github.com/GaodengFan/MsMorph