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Abstract #0527

Unsupervised 3D registration for multi-tasks with local-global self-attention training

Zhengyong Huang1,2, Ning Jiang1,2, and Yao Sui1,2
1National Institute of Health Data Science, Peking University, Beijing, China, 2Institute of Medical Technology, Peking University, Beijing, China

Synopsis

Keywords: Data Processing, Analysis/Processing

Motivation: Image registration is crucial in medical analysis, but traditional iterative methods are time-consuming. While deep-learning techniques are popular, they often underperform in different tasks.

Goal(s): The study aims to develop a method that ensures stable and reliable registration accuracy across various scenarios.

Approach: We propose LGANet, a deformable image registration method that integrates a dual-stream pyramidal network and local-global self-attention. It initializes the deformation field with a local-global attention module and refines it in a coarse-to-fine manner through feature interaction and fusion.

Results: The results tested on three public MRI datasets show that our method achieves superior performance over several start-of-the-art registration methods.

Impact: We presents an unsupervised 3D image registration method based on deep learning principles. By incorporating local-global self-attention training, this method is robust across different registration tasks. It ensures consistently accurate registration results and is applicable to multi clinical registration scenarios.

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Keywords