波谱学杂志2026，Vol.43Issue(3)：291-306,16.DOI:10.11938/cjmr20253190
基于多尺度膨胀残差和双重注意力的可变形配准网络
Deformable Registration Network Based on Multi-scale Dilated Residual and Dual Attention
摘要
Abstract
Deformable registration plays a significant role in multiple medical image analysis tasks.However,the fixed receptive field of convolutional neural networks makes it difficult to fully capture the spatial context information of the brain.While the Transformer architecture can effectively capture global information,it suffers from high computational cost.To address this dilemma,we propose a deformable registration network based on multi-scale dilated residual convolution and dual attention.This network employs a multi-scale dilated residual convolution to capture local details and extensive context information simultaneously.A dual attention module is integrated to enhance feature representation capability.The decoder section introduces dynamic upsampling to precisely reconstruct high-frequency details,thereby ensuring the topological integrity of the deformation field.Experiments were conducted on two public brain datasets.The results demonstrate that the proposed model achieves high registration performance across multiple metrics,including Dice similarity coefficient(DSC),95%Hausdorff distance(HD95),and percentage of non-positive Jacobian determinant,indicating superior registration precision and smoother deformation fields.关键词
可变形图像配准/多尺度/双重注意力/动态上采样Key words
deformable image registration/multi-scale/dual attention/dynamic upsampling分类
数理科学引用本文复制引用
羊晶晶,王远军..基于多尺度膨胀残差和双重注意力的可变形配准网络[J].波谱学杂志,2026,43(3):291-306,16.基金项目
上海市自然科学基金资助项目(18ZR1426900). （18ZR1426900）
