Retinal Vessel Segmentation is important for the diagnosis of various diseases. The research on retinal vessel segmentation focuses mainly on the improvement of the segmentation model which is usually based on U-Net architecture. In our study, we use the U-Net architecture and we rely on heavy data augmentation in order to achieve better performance. The success of the data augmentation relies on successfully addressing the problem of input images. By analyzing input images and performing the augmentation accordingly we show that the performance of the U-Net model can be increased dramatically. Results are reported using the most widely used retina dataset, DRIVE.
翻译：视网膜船只分离研究对诊断各种疾病十分重要。视网膜船只分离研究主要侧重于改进通常基于 U-Net 结构的分解模型。在研究中,我们使用U-Net 结构,依靠重数据增强来取得更好的性能。数据增强的成功取决于成功解决输入图像问题。通过分析输入图像和相应进行扩增,我们表明U-Net 模型的性能可以大幅提高。结果报告使用最广泛使用的视网膜数据集,即Dive。
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