关键词: 图像配准, 医学图像分析, 频谱分析, 可逆神经网络, 深度学习

Abstract: Deformable registration was regarded as a key task in medical image processing, whose performance directly affected the accuracy of subsequent tasks such as segmentation, classification, and prediction. However, due to the insensitivity of neural networks to high-frequency components, existing methods had difficulty capturing high-frequency information in images, which affected the fitting accuracy of the deformation field. To address these issues, a high-frequency-preserving medical image registration model based on frequency spectrum decomposition—DFRes (Decomposition in Frequency domain model for Registration), was proposed. A frequency decomposition strategy was introduced, and a dual-branch structure was adopted to process high-frequency and low-frequency information from the original image. Meanwhile, an invertible neural network structure with high-frequency preservation characteristics and a bridge-style feature fusion module with the ability to fuse high- and low-frequency information were designed. Alternating spatial-frequency information extraction modules were further incorporated to enhance the model’s ability to extract and fuse frequency-domain and spatial-domain information. Comparative experiments were conducted on the IXI, OSSAI, and Huaxi rectal cancer datasets against existing advanced models. DFRes achieved significant improvements on multiple metrics: compared to the suboptimal TransMorph model, Dice Similarity Coefficient (DSC) was increased by 2.5 percentage points, Average Surface Distance (ASD) was reduced by 1.2 percentage points, and SSIM was increased by 1.6 percentage points. Ablation experiments were also conducted to verify the effectiveness of the module design.

Key words: image registration, medical image analysis, spectrum analysis, Inverse Neural Network (INN), deep learning