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Anisotropy arises due to the influence of scanning equipment and parameters, resulting in a distance between slices that is often much greater than the actual distance represented by a single pixel within each slice. This can lead to inefficiency or ineffectiveness in 3D convolution. To address the anisotropy issue, we propose FOrViG, an asymmetric vision graph neural network (GNN) framework that captures the correlation between different slices by constructing a graph for multi-slice images and aggregating information from adjacent nodes. This allows FOrViG to efficiently extract 3D spatial scale information, and effectively identify feature nodes associated with small lesions efficiently, thereby improving the accuracy of lesion segmentation on anisotropic 3D multimodal MR images. As far as we know, this is the first study that adopts GNN to address anisotropy issues. Additionally, we also design a factorized …