Deformable image registration with attention-guided fusion of multi-scale deformation fields

Abstract

Deformable medical image registration plays a crucial role in theoretical research and clinical application. Traditional methods suffer from low registration accuracy and efficiency. Recent deep learning-based methods have made significant progresses, especially those weakly supervised by anatomical segmentations. However, the performance still needs further improvement, especially for images with large deformations. This work proposes a novel deformable image registration method based on an attention-guided fusion of multi-scale deformation fields. Specifically, we adopt a separately trained segmentation network to segment the regions of interest to remove the interference from the uninterested areas. Then, we construct a novel dense registration network to predict the deformation fields of multiple scales and combine them for final registration through an attention-weighted field fusion process. The proposed contour loss and image structural similarity index (SSIM) based loss further enhance the model training through regularization. Compared to the state-of-the-art methods on three benchmark datasets, our method has achieved significant performance improvement in terms of the average Dice similarity score (DSC), Hausdorff distance (HD), Average symmetric surface distance (ASSD), and Jacobian coefficient (JAC). For example, the improvements on the SHEN dataset are 0.014, 5.134, 0.559, and 359.936, respectively.