TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration

Abstract

Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration methods, current approaches still face considerable challenges, such as insufficient capture of local details, difficulty in effectively modeling global contextual information, and limited robustness in handling complex deformations. These limitations hinder the precision of high-resolution registration, particularly when dealing with medical images with intricate structures. To address these issues, this paper presents a novel registration network (TCDE-Net), an unsupervised medical image registration method based on a dual-encoder architecture. The dual encoders complement each other in feature extraction, enabling the model to effectively handle large-scale nonlinear deformations and capture intricate local details, thereby enhancing registration accuracy. Additionally, the detail-enhancement attention module aids in restoring fine-grained features, improving the network's capability to address complex deformations such as those at gray-white matter boundaries. Experimental results on the OASIS, IXI, and Hammers-n30r95 3D brain MR dataset demonstrate that this method outperforms commonly used registration techniques across multiple evaluation metrics, achieving superior performance and robustness. Our code is available at https://github.com/muzidongxue/TCDE-Net.