Retinal Image Segmentation with a Structure-Texture Demixing Network

TL;DR

This paper introduces STD-Net, a novel neural network that separates structure and texture in retinal images, leading to improved segmentation accuracy for disease diagnosis.

Contribution

The paper proposes a structure-texture demixing network that processes structures and textures separately, enhancing retinal image segmentation performance.

Findings

Effective in blood vessel segmentation

Improves optic disc and cup segmentation

Demonstrates superior results on two tasks

Abstract

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.