Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

中文翻译：

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通过广泛激活的深度残差学习进行视网膜血管分割


目的。视网膜血管图像分割是眼科分析的重要步骤。然而，由于血管对比度低、特征信息复杂，难以准确分割小血管。本研究的目的是开发一种改进的视网膜血管分割结构（WA-Net）来克服这些挑战。方法。本文主要关注深度学习的宽度。 ResNet 块的通道被拓宽以传播更多低级特征，并且身份映射路径被精简以保持参数复杂性。使用残留的空洞空间金字塔模块来捕获不同尺度的视网膜血管。我们应用权重归一化来消除小批量的影响并提高分割精度。实验是在 DRIVE 和 STARE 数据集上进行的。为了展示 WA-Net 的通用性，我们在数据集之间进行了交叉训练。结果。数据集中的全局准确度和特异性分别为 95.66% 和 96.45%、98.13% 和 98.71%。与相应的内部数据集的性能相比，数据集间的准确度和曲线下面积仅相差 1%∼2%。结论。所有结果表明，WA-Net提取了更详细的血管，并在视网膜血管分割任务上表现出优越的性能。