Boosting 2D brain image registration via priors from large model.

Abstract

Background: Deformable medical image registration aims to align image pairs with local differences, improving the accuracy of medical analyses and assisting various diagnostic scenarios.

Purpose: We aim to overcome these challenges: Deep learning-based registration approaches have greatly enhanced registration speed and accuracy by continuously improving registration networks and processes. However, the lack of extensive medical datasets limits the complexity of registration models. Optimizing registration networks within a fixed dataset often leads to overfitting, hindering further accuracy improvements and reducing generalization capabilities.

Methods: We explore the application of the foundational model DINOv2 to registration tasks, leveraging its prior knowledge to support learning-based unsupervised registration networks and overcome network bottlenecks to improve accuracy. We investigate three modes of DINOv2-assisted registration, including direct registration architecture, enhanced architecture, and refined architecture. Additionally, we study the applicability of three feature aggregation methods-convolutional interaction, direct fusion, and cross-attention-within the proposed DINOv2-based registration frameworks.

Results: We conducted extensive experiments on the IXI and OASIS public datasets, demonstrating that the enhanced and refined architectures notably improve registration accuracy, reduce data dependency, and maintain strong generalization capabilities.

Conclusion: This study offers novel approaches for applying foundational models to deformable image registration tasks.