TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration.
Authors
Affiliations (5)
Affiliations (5)
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong 510060, China; Collaborative Innovation Center for Cancer Medicine, China; State Key Laboratory of Oncology in South China, China; Sun Yat-sen University Cancer Center, China; Department of Radiation Oncology, Guangzhou, Guangdong 510060, China. Electronic address: [email protected].
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China. Electronic address: [email protected].
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China. Electronic address: [email protected].
- Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong 510060, China; Collaborative Innovation Center for Cancer Medicine, China; State Key Laboratory of Oncology in South China, China; Sun Yat-sen University Cancer Center, China; Department of Radiation Oncology, Guangzhou, Guangdong 510060, China. Electronic address: [email protected].
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, Guangdong 510631, China. Electronic address: [email protected].
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.