Medical Image Fusion Based on Multilevel Bidirectional Feature Interaction Network

Medical image fusion integrates multi-modal images with complementary information to enhance the image quality in clinical diagnosis. This process typically involves three steps including feature extraction, feature fusion, and image reconstruction. However, most image fusion methods have limitations in simultaneously extracting specific and shared information from different-modal images while considering cross-modal interaction, leading to incomplete feature extraction and fusion. Besides, the multi-level features interaction of most existing methods is insufficient, leading to defective utilization of fused information under different receptive fields. To address these issues, we propose an end-to-end medical image fusion network based on multi-level bidirectional feature interaction. The bidirectional feature interaction is manifested vertically and horizontally. Firstly, we design a cross-modal interactive feature extraction module based on two modality-independent branches and a modality-shared branch, achieving vertical feature interaction between different-modal features at the same level. Besides, we proposed a forward and backward interaction module (FBIM) to achieve multi-level horizontal feature interaction, which can bridge the gap of multi-level fused features and promote the utilization of fused information under different receptive fields. Additionally, we design a fusion module to generate two weight maps based on the modality-shared branch to fuse two modality-independent features. Qualitative and quantitative experiments demonstrate the superiority of our method over the state-of-the-art fusion methods.