  Accurate segmentation of the retinogeniculate visual pathway (RGVP) aids in
the diagnosis and treatment of visual disorders by identifying disruptions or
abnormalities within the pathway. However, the complex anatomical structure and
connectivity of RGVP make it challenging to achieve accurate segmentation. In
this study, we propose a novel Modality Exchange Network (ME-Net) that
effectively utilizes multi-modal magnetic resonance (MR) imaging information to
enhance RGVP segmentation. Our ME-Net has two main contributions. Firstly, we
introduce an effective multi-modal soft-exchange technique. Specifically, we
design a channel and spatially mixed attention module to exchange modality
information between T1-weighted and fractional anisotropy MR images. Secondly,
we propose a cross-fusion module that further enhances the fusion of
information between the two modalities. Experimental results demonstrate that
our method outperforms existing state-of-the-art approaches in terms of RGVP
segmentation performance.
