GLER-Unet: An ensemble network for hard exudates segmentation

Abstract

The detection of hard exudation in diabetic retinopathy is a hot topic in medical image segmentation. Aiming at the irregular shape and different size of lesion area in Hard Exudates segmentation task and the common few-shot learning challenge in medical image segmentation task, a Global-Local Ensemble Robust U-Net is proposed. The network consists of a Global Contour Extraction network for extracting long-range semantics and hard exudates contour which use complete image for training, a Local Refined Feature Segmentation network for extracting local refined segmentation rules which use patch image for training, and a Feature Revise network for fusing the features extracted by the first two networks and generating binary masks. The proposed method obtains DICE, TPR and PPV of 0.8741, 0.8752, 0.8730 and 0.8960, 0.8964, 0.8956 respectively on E-Ophtha and IDRiD. At the same time, the proposed methods shows strong robustness in cross dataset testing, better than other baseline models.