Cup-to-disc ratio (CDR) is of great importance during assessing structural changes at the optic nerve head (ONH) and diagnosis of glaucoma. While most efforts have been put on acquiring the CDR number through CNN-based segmentation algorithms followed by the calculation of CDR, these methods usually only focus on the features in the convolution kernel, which is, after all, the operation of the local region, ignoring the contribution of rich global features (such as distant pixels) to the current features. In this paper, a new end-to-end channel and spatial attention regression deep learning network is proposed to deduces CDR number from the regression perspective and combine the self-attention mechanism with the regression network. Our network consists of four modules: the feature extraction module to extract deep features expressing the complicated pattern of optic disc (OD) and optic cup (OC), the attention module including the channel attention block (CAB) and the spatial attention block (SAB) to improve feature representation by aggregating long-range contextual information, the regression module to deduce CDR number directly, and the segmentation-auxiliary module to focus the model’s attention on the relevant features instead of the background region. Especially, the CAB selects relatively important feature maps in channel dimension, shifting the emphasis on the OD and OC region; meanwhile, the SAB learns the discriminative ability of feature representation at pixel level by capturing the relationship of intra-feature map. The experimental results of ORIGA dataset show that our method obtains absolute CDR error of 0.067 and the Pearson’s correlation coefficient of 0.694 in estimating CDR and our method has a great potential in predicting the CDR number.

中文翻译：

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渠道与空间注意力回归网络的杯碟比估计

杯碟比（CDR）在评估视神经头（ONH）的结构变化和青光眼的诊断过程中非常重要。尽管人们已尽最大努力通过基于CNN的分段算法来获取CDR编号，然后再计算CDR，但这些方法通常仅关注卷积内核中的特征，毕竟毕竟是局部区域的操作，忽略丰富的全局特征（例如远距离像素）对当前特征的贡献。本文提出了一种新的端到端通道和空间注意力回归深度学习网络，从回归的角度推导CDR数，并将自我注意机制与回归网络相结合。我们的网络包含四个模块：特征提取模块提取表达复杂视盘（OD）和视杯（OC）模式的深层特征，注意模块包括通道注意块（CAB）和空间注意块（SAB），以通过聚合来改善特征表示远程上下文信息，回归模块直接推导CDR数，以及分段辅助模块以将模型的注意力集中在相关特征上，而不是背景区域上。特别是，CAB在通道维度上选择了相对重要的特征图，从而将重点转移到了OD和OC区域。同时，SAB通过捕获特征内映射的关系来学习像素级特征表示的判别能力。