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Glaucoma is a leading cause of irreversible blindness. Accurate optic disc (OD) and optic cup (OC) segmentation in fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks have demonstrated promising progress in OD and OC joint segmentation in fundus images. However, the segmentation of OC is a challenge due to the low contrast and blurred boundary. In this paper, we propose an improved U-shape based network to jointly segment OD and OC. There are three main contributions: (1) The efficient channel attention (ECA) blocks are embedded into our proposed network to avoid dimensionality reduction and capture cross-channel interaction in an efficient way. (2) A multiplexed dilation convolution (MDC) module is proposed to extract more target features with various sizes and preserve more spatial information. (3) Three global context extraction (GCE) modules are used in our network. By introducing multiple GCE modules between encoder and decoder, the global semantic information flow from high-level stages can be gradually guided to different stages. The method proposed in this paper was tested on 240 fundus images. Compared with U-Net, Attention U-Net, Seg-Net and FCNs, the OD and OC’s mean Dice similarity coefficient of the proposed method can reach 96.20% and 90.00% respectively, which are better than the above networks.
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