Modality Exchange Network for Retinogeniculate Visual Pathway Segmentation

• URL: http://arxiv.org/abs/2401.01685v1
• Date: Wed, 3 Jan 2024 11:41:57 GMT
• Title: Modality Exchange Network for Retinogeniculate Visual Pathway Segmentation
• Authors: Hua Han (1 and 2), Cheng Li (1), Lei Xie (3), Yuanjing Feng (3), Alou Diakite (1 and 2), Shanshan Wang (1 and 4) ((1) Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, (2) University of Chinese Academy of Sciences, Beijing, China, (3) College of Information Engineering, Zhejiang University of Technology, Hangzhou, China, (4) Peng Cheng Laboratory, Shenzhen, China)
• Abstract summary: We propose a novel Modality Exchange Network (ME-Net) that effectively utilizes multi-modal magnetic resonance (MR) imaging information to enhance RGVP segmentation. Specifically, we design a channel and spatially mixed attention module to exchange modality information between T1-weighted and fractional anisotropy MR images. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches in terms of RGVP segmentation performance.
• Score: 5.726588626363204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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