  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.
