Automatic segmentation of macular edema in retinal OCT images using improved U-Net

Abstract

The number and volume of retinal macular edemas are important indicators for screening and diagnosing retinopathy. Aiming at the problem that the segmentation method of macular edemas in a retinal optical coherence tomography (OCT) image is not ideal in segmentation of diverse edemas, this paper proposes a new method of automatic segmentation of macular edema regions in retinal OCT images using the improved U-Net++. The proposed method makes full use of the U-Net++ re-designed skip pathways and dense convolution block; reduces the semantic gap of the feature maps in the encoder/decoder sub-network; and adds the improved Resnet network as the backbone, which make the extraction of features in the edema regions more accurate and improves the segmentation effect. The proposed method was trained and validated on the public dataset of Duke University, and the experiments demonstrated the proposed method can not only improve the overall segmentation effect, but also can significantly improve the segmented precision for diverse edema in multi-regions, as well as reducing the error of the number of edema regions.