Glaucoma is the leading cause of irreversible blindness. For glaucoma screening, the cup to disc ratio (CDR) is a significant indicator, whose calculation relies on the segmentation of optic disc(OD) and optic cup(OC) in color fundus images. This study proposes a residual multi-scale convolutional neural network with a context semantic extraction module to jointly segment the OD and OC. The proposed method uses a W-shaped backbone network, including image pyramid multi-scale input with the side output layer as an early classifier to generate local prediction output. The proposed method includes a context extraction module that extracts contextual semantic information from multiple level receptive field sizes and adaptively recalibrates channel-wise feature responses. It can effectively extract global information and reduce the semantic gaps in the fusion of deep and shallow semantic information. We validated the proposed method on four datasets, including DRISHTI-GS1, REFUGE, RIM-ONE r3, and a private dataset. The overlap errors are 0.0540, 0.0684, 0.0492, 0.0511 in OC segmentation and 0.2332, 0.1777, 0.2372, 0.2547 in OD segmentation, respectively. Experimental results indicate that the proposed method can estimate the CDR for a large-scale glaucoma screening.

青光眼是导致不可逆失明的主要原因。对于青光眼筛查，杯盘比（CDR）是一个重要指标，其计算依赖于彩色眼底图像中视盘（OD）和视杯（OC）的分割。本研究提出了一种带有上下文语义提取模块的残差多尺度卷积神经网络来联合分割 OD 和 OC。所提出的方法使用W形骨干网络，包括图像金字塔多尺度输入和侧输出层作为早期分类器来生成局部预测输出。所提出的方法包括一个上下文提取模块，该模块从多级感受野大小中提取上下文语义信息，并自适应地重新校准通道特征响应。它可以有效地提取全局信息，减少深浅语义信息融合中的语义差距。我们在四个数据集上验证了所提出的方法，包括 DRISHTI-GS1、REFUGE、RIM-ONE r3 和一个私有数据集。OC 分割的重叠误差分别为 0.0540、0.0684、0.0492、0.0511，OD 分割的重叠误差分别为 0.2332、0.1777、0.2372、0.2547。实验结果表明，所提出的方法可以估计用于大规模青光眼筛查的CDR。