CLC-Net: Contextual and local collaborative network for lesion segmentation in diabetic retinopathy images

Diabetic retinopathy (DR) is the leading cause of blindness among people of working age. Fundus lesions are clinical signs of DR, and their recognition and delineation are important for early screening, grading, and monitoring of the disease. We propose in this work a fully automatic deep convolutional neural network method for simultaneous segmentation of four different types of DR-related fundus lesions. To exploit multi-scale image information, we propose a collaborative architecture that comprises a contextual branch and a local branch. An attention mechanism is designed to fuse feature maps from all decoding layers in order to effectively and fully combine informative features from the two branches. Moreover, an auxiliary classification task with a novel supervision scheme is introduced to reduce model overfitting and further improve the accuracy of lesion segmentation. Extensive experiments are conducted using three public fundus datasets, and our method produces a mean AUC value of 0.677, 0.629, and 0.581 on them respectively. The results demonstrate the advantages of the proposed method, outperforming alternative strategies and other state-of-the-art methods in the literature.