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.

翻译：视网膜膝状体视觉通路的精准分割有助于通过识别通路内的中断或异常来诊断和治疗视觉障碍。然而，RGVP复杂的解剖结构和连通性使得实现精准分割极具挑战性。本研究提出了一种新颖的模态交换网络，该网络有效利用多模态磁共振成像信息以增强RGVP分割。我们的ME-Net包含两大贡献：首先，我们引入了一种高效的多模态软交换技术，具体设计了通道与空间混合注意力模块，以在T1加权磁共振图像与分数各向异性磁共振图像之间交换模态信息；其次，我们提出了一种交叉融合模块，进一步增强了两种模态之间的信息融合。实验结果表明，我们的方法在RGVP分割性能上优于现有最先进方法。