Quick and Automatic Segmentation of Retinal Layers in OCT Images Using Residual and Attention U-net

Abstract

Computer image processing technology is used to segment the retinal image automatically and compute the thickness of each layer. The thickness can be used to assess various retinal diseases directly. In order to segment retinal layers quickly and accurately, a new segmentation method combining RAU-net (Residual and Attention U-net, RAU-net) and graph search is proposed. This method adds residual block structure and attention gate structure to the basis of U-net. The residual block structure avoids the problems of gradient disappearance and gradient explosion effectively while obtaining advanced features by building deeper networks. Models trained with attention gate structure highlights the learning of salient features of retinal images. The segmentation results obtained after the model prediction gain nine boundary regions of interest, and then the graph search method is used to optimize the layer boundary for accurate retinal layer. The results show that the error between the RAU-net algorithm and manual segmentation is just about one pixel, and it only takes 4 s to complete segmentation of an OCT retinal image. Combining RAU-net and the graph search algorithm, the method provides a fast and accurate quantitative analysis tool for the clinical diagnosis and treatment of retinal diseases.