  In the realm of medical image fusion, integrating information from various
modalities is crucial for improving diagnostics and treatment planning,
especially in retinal health, where the important features exhibit differently
in different imaging modalities. Existing deep learning-based approaches
insufficiently focus on retinal image fusion, and thus fail to preserve enough
anatomical structure and fine vessel details in retinal image fusion. To
address this, we propose the Topology-Aware Graph Attention Network (TaGAT) for
multi-modal retinal image fusion, leveraging a novel Topology-Aware Encoder
(TAE) with Graph Attention Networks (GAT) to effectively enhance spatial
features with retinal vasculature's graph topology across modalities. The TAE
encodes the base and detail features, extracted via a Long-short Range (LSR)
encoder from retinal images, into the graph extracted from the retinal vessel.
Within the TAE, the GAT-based Graph Information Update (GIU) block dynamically
refines and aggregates the node features to generate topology-aware graph
features. The updated graph features with base and detail features are combined
and decoded as a fused image. Our model outperforms state-of-the-art methods in
Fluorescein Fundus Angiography (FFA) with Color Fundus (CF) and Optical
Coherence Tomography (OCT) with confocal microscopy retinal image fusion. The
source code can be accessed via https://github.com/xintian-99/TaGAT.
