An improved U-shape neural network for soft exudate segmentation

Abstract

Diabetic Retinopathy (DR) is a complication with a high blindness rate caused by diabetes. The diagnosis of DR requires examining the patient's fundus several times a year, which is a heavy burden for a patient and consumes a lot of medical resources. Since soft exudate is an early indicator for detecting the presence of DR, an automated and exact segmentation method for soft exudate is helpful for making a rapid diagnosis. Despite recent advances in medical image processing, the segmentation method of soft exudate is still unsatisfactory due to the limited amount of soft exudate data, imbalanced categories, varying scales and so on. In this work, an improved U-shape neural network (IUNet) was proposed according to the characteristic of soft exudate, which consisted of a contracting path and a symmetric expanding path. Both were composed of convolutional layers, multi-scale modules, and shortcut connections. In training process, a data enhancement strategy was used to generate more training data and a weighted cross-entropy loss function to suppress positive and negative sample imbalance. The proposed method had excellent performance on soft exudate task in Indian Diabetic Retinopathy Image Dataset (IDRiD). The area under precision-recall (AUPR) curve score was 0.711, which was superior to the state-of-the-art models.