Generative Adversarial Network for Trimodal Medical Image Fusion Using Primitive Relationship Reasoning

Medical image fusion has become a hot biomedical image processing technology in recent years. The technology coalesces useful information from different modal medical images onto an informative single fused image to provide reasonable and effective medical assistance. Currently, research has mainly focused on dual-modal medical image fusion, and little attention has been paid on trimodal medical image fusion, which has greater application requirements and clinical significance. For this, the study proposes an end-to-end generative adversarial network for trimodal medical image fusion. Utilizing a multi-scale squeeze and excitation reasoning attention network, the proposed method generates an energy map for each source image, facilitating efficient trimodal medical image fusion under the guidance of an energy ratio fusion strategy. To obtain the global semantic information, we introduced squeeze and excitation reasoning attention blocks and enhanced the global feature by primitive relationship reasoning. Through extensive fusion experiments, we demonstrate that our method yields superior visual results and objective evaluation metric scores compared to state-of-the-art fusion methods. Furthermore, the proposed method also obtained the best accuracy in the glioma segmentation experiment.