BACKGROUND Retinal blood vessel segmentation has an important guiding significance for the analysis and diagnosis of cardiovascular diseases such as hypertension and diabetes. But the traditional manual method of retinal blood vessel segmentation is not only time-consuming and laborious but also cannot guarantee the accuracy and efficiency of diagnosis. Therefore, it is especially significant to create a computer-aided method of automatic and accurate retinal vessel segmentation. METHODS In order to extract the blood vessels' contours of different diameters to realize fine segmentation of retinal vessels, we propose a Bidirectional Symmetric Cascade Network (BSCN) where each layer is supervised by vessel contour labels of specific diameter scale instead of using one general ground truth to train different network layers. In addition, to increase the multi-scale feature representation of retinal blood vessels, we propose the Dense Dilated Convolution Module (DDCM), which extracts retinal vessel features of different diameters by adjusting the dilation rate in the dilated convolution branches and generates two blood vessel contour prediction results by two directions respectively. All dense dilated convolution module outputs are fused to obtain the final vessel segmentation results. RESULTS We experimented the three datasets of DRIVE, STARE, HRF and CHASE_DB1, and the proposed method reaches accuracy of 0.9846/0.9872/0.9856/0.9889 and AUC of 0.9874/0.9941/0.9882/0.9874 on DRIVE, STARE, HRF and CHASE_DB1. CONCLUSIONS The experimental results show that compared with the state-of-art methods, the proposed method has strong robustness, it not only avoids the adverse interference of the lesion background but also detects the tiny blood vessels at the intersection accurately.

中文翻译：

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BSCN：用于视网膜血管分割的双向对称级联网络。

背景技术视网膜血管分割对心血管疾病如高血压和糖尿病的分析和诊断具有重要的指导意义。但是传统的人工视网膜血管分割方法不仅费时费力，而且不能保证诊断的准确性和效率。因此，建立一种计算机辅助的自动和准确的视网膜血管分割方法尤为重要。方法为了提取不同直径的血管轮廓以实现视网膜血管的精细分割，我们提出了双向对称级联网络（BSCN），其中每一层都由具有特定直径比例的血管轮廓标签监督，而不是使用一个通用的地面训练不同网络层的真理。此外，为了增加视网膜血管的多尺度特征表示，我们提出了密集扩张卷积模块（DDCM），该模块通过调节扩张卷积分支中的扩张率来提取不同直径的视网膜血管特征，并生成两个血管轮廓预测结果通过两个方向。将所有密集的扩张卷积模块输出融合，以获得最终的血管分割结果。结果我们对DRIVE，STARE，HRF和CHASE_DB1的三个数据集进行了实验，所提出的方法在DRIVE，STARE，HRF和CHASE_DB1上的精度达到0.9846 / 0.9872 / 0.9856 / 0.9889和AUC分别为0.9874 / 0.9941 / 0.9882 / 0.9874。结论实验结果表明，与现有方法相比，该方法具有较强的鲁棒性，