The article introduces a novel approach to 2D/3D image registration using a correlation-driven network. Traditional optimization-based techniques are compared with the proposed method, showcasing superior performance. The dual-branch CNN-Transformer encoder is highlighted for its ability to extract and separate local and global features effectively. The training strategy focuses on approximating a convex-shaped similarity function, enhancing the overall registration process. Experimental results demonstrate the robustness and effectiveness of the proposed method, indicating its potential for further research and application in medical imaging.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Minheng Chen... at arxiv.org 03-18-2024
https://arxiv.org/pdf/2402.02498.pdfDeeper Inquiries