Automatic Layering of Retinal OCT Images with Dual Attention Mechanism

Abstract

At present, there are more and more people suffering from retinal diseases. Doctors can diagnose and prevent eye diseases by observing the changes in the thickness of the retinal layer in OCT images. Due to the low contrast of the retinal layer boundary of the OCT image, manual segmentation is time-consuming and laborious. Moreover, most of the current automatic retinal layer segmentation methods are based on traditional methods and the segmentation result is not good. Therefore, in this paper, we proposed an end-to-end automatic retinal layer segmentation method based on deep learning, called DA-PSPNet, which can accurately segment seven retinal layers in OCT images. DA-PSPNet integrates a dual attention mechanism based on the PSPNet network, aiming to extract richer layer boundary information. It merges features of various levels to aggregate contextual information in different regions. The experimental results show that the proposed method achieves better performance in several evaluation indexes compared with the other four mainstream segmentation networks.