RANet: An end-to-end residual-aware adaptive registration network for echocardiogram
Year: 2026
Type: article
Abstract: Echocardiographic registration is crucial for cardiac disease screening, but challenges such as low contrast, speckle noise, and motion artifacts complicate the deformable registration task. Current achievements mainly focus on intro-subject left ventricle motion tracking, while facing challenges of registering narrow and vague myocardial wall. To overcome these limitations, we propose a more universal residual-aware adaptive registration network (RANet), exhibiting good performance on main structures like ventricular, myocardium, and atria. This end-to-end framework utilizes parallel adaptive wavelet convolution (WTConv)-convolutional neural network (CNN) fusion in its parallel enhanced encoder (PEE) to extract complementary global-local features at multiple scales, strategically directs attention to misaligned structural edges through residual features in the multi-head residual attention (MRA) module, and integrates channel-space-contextual attention with learnable weights via the adaptive collaborative attention (ACA) module for noise-robust feature fusion and adaptive contribution balancing across hierarchies. Experiments are evaluated on three datasets. The results demonstrate that RANet outperforms state-of-the-art methods in both accuracy and robustness.
Authors: Yushi Li, Qian Hu, Xiangbo Lin, Xue Li, Yu Dong, Tong Lin
Institution: Dalian University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,590
OpenAlex ID: W7130954490