CcNet: A cross-connected convolutional network for segmenting retinal vessels using multi-scale features

Retinal vessel segmentation (RVS) helps the diagnosis of diabetic retinopathy, which can cause visual impairment and even blindness. Some problems are hindering the application of automatic RVS, including accuracy, robustness and segmentation speed. In this paper, we propose a cross-connected convolutional neural network (CcNet) for the automatic segmentation of retinal vessel trees. In the CcNet, convolutional layers extract the features and predict the pixel classes according to those learned features. The CcNet is trained and tested with full green channel images directly. The cross connections between primary path and secondary path fuse the multi-level features. The experimental results on two publicly available datasets (DRIVE: Sn = 0.7625, Acc = 0.9528; STARE: Sn = 0.7709, Acc = 0.9633) are higher than those of most state-of-the-art methods. In the cross-training phase, CcNte's accuracy fluctuations (Delta Accs) on DRIVE and STARE are 0.0042 and 0.007, respectively, which are relatively small compared with those of published methods. In addition, our algorithm has faster computing speed (0.063 s) than those listed algorithms using a GPU (graphics processing unit). These results reveal that our algorithm has potential in practical applications due to promising segmentation performances including advanced specificity, accuracy, robustness and fast processing speed. (c) 2019 Elsevier B.V. All rights reserved.