DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel.

ABSTRACT:

Background

Many fundus imaging modalities measure ocular changes. Automatic retinal vessel segmentation (RVS) is a significant fundus image-based method for the diagnosis of ophthalmologic diseases. However, precise vessel segmentation is a challenging task when detecting micro-changes in fundus images, e.g., tiny vessels, vessel edges, vessel lesions and optic disc edges.

Methods

In this paper, we will introduce a novel double branch fusion U-Net model that allows one of the branches to be trained by a weighting scheme that emphasizes harder examples to improve the overall segmentation performance. A new mask, we call a hard example mask, is needed for those examples that include a weighting strategy that is different from other methods. The method we propose extract

SUBMITTER: Huang J 

PROVIDER: S-EPMC9044242 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature