论文标题
带有结构文本解散网络的视网膜图像分割
Retinal Image Segmentation with a Structure-Texture Demixing Network
论文作者
论文摘要
视网膜图像分割在自动疾病诊断中起重要作用。此任务非常具有挑战性，因为复杂的结构和纹理信息混合在视网膜图像中，并且很难区分信息。现有方法共同处理纹理和结构，这可能导致偏见的模型识别纹理，从而导致较低的分割性能。为了解决这个问题，我们提出了一种分割策略，该策略试图将结构和纹理组成部分分开，并显着提高性能。为此，我们设计了一个结构性纹理网络（STD-NET），该网络可以对结构和纹理进行不同，更好地处理结构和纹理。对两个视网膜图像分割任务（即血管分割，视盘和杯子分割）进行了广泛的实验，证明了该方法的有效性。
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.
