Retinal blood vessel extraction is considered to be the indispensable action for the diagnostic purpose of many retinal diseases. In this work, a parallel fully convolved neural network-based architecture is proposed for the retinal blood vessel segmentation. Also, the network performance improvement is studied by applying different levels of preprocessed images. The proposed method is experimented on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the Retina) which are the widely accepted public database for this research area. The proposed work attains high accuracy, sensitivity, and specificity of about 96.37%, 86.53%, and 98.18% respectively. Data independence is also proved by testing abnormal STARE images with DRIVE trained model. The proposed architecture shows better result in the vessel extraction irrespective of vessel thickness. The obtained results show that the proposed work outperforms most of the existing segmentation methodologies, and it can be implemented as the real time application tool since the entire work is carried out on CPU. The proposed work is executed with low-cost computation; at the same time, it takes less than 2 s per image for vessel extraction.

中文翻译：

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用于视网膜血管分割的全卷积神经网络并行架构。

对于许多视网膜疾病的诊断目的，视网膜血管抽出被认为是必不可少的动作。在这项工作中，提出了基于并行完全卷积神经网络的视网膜血管分割架构。此外，通过应用不同级别的预处理图像来研究网络性能的提高。该方法在DRIVE（用于血管提取的数字视网膜图像）和STARE（视网膜结构分析）上进行了实验，这是该研究领域广为接受的公共数据库。拟议的工作获得了大约96.37％，86.53％和98.18％的高准确性，敏感性和特异性。通过使用DRIVE训练模型测试异常的STARE图像，也证明了数据独立性。所提出的架构显示出与血管厚度无关的更好的血管提取结果。获得的结果表明，所提出的工作优于大多数现有的分割方法，并且由于整个工作都是在CPU上进行的，因此可以作为实时应用工具来实现。拟议的工作是通过低成本计算执行的；同时，每个图像的提取时间少于2 s。