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arXiv 2008.008172020-07-15eess.IVcs.CV

Retinal Image Segmentation with a Structure-Texture Demixing Network

South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）

Shihao Zhang, Huazhu Fu, Yanwu Xu, Yanxia Liu, Mingkui Tan

备注：: Accepted to MICCAI 2020

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Retinal image segmentation plays an important role in automatic disease diagnosis. This task is very challenging because the complex structure and texture information are mixed in a retinal image, and distinguishing the information is difficult. Existing methods handle texture and structure jointly, which may lead biased models toward recognizing textures and thus results in inferior segmentation performance. To address it, we propose a segmentation strategy that seeks to separate structure and texture components and significantly improve the performance. To this end, we design a structure-texture demixing network (STD-Net) that can process structures and textures differently and better. Extensive experiments on two retinal image segmentation tasks (i.e., blood vessel segmentation, optic disc and cup segmentation) demonstrate the effectiveness of the proposed method.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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