Background and objective

Deep learning techniques are instrumental in developing network models that aid in the early diagnosis of life-threatening diseases. To screen and diagnose the retinal fundus and coronary blood vessel disorders, the most important step is the proper segmentation of the blood vessels.

Methods

This paper aims to segment the blood vessels from both the coronary angiogram and the retinal fundus images using a single VSSC Net after performing the image-specific preprocessing. The VSSC Net uses two-vessel extraction layers with added supervision on top of the base VGG-16 network. The vessel extraction layers comprise of the vessel-specific convolutional blocks to localize the blood vessels, skip chain convolutional layers to enable rich feature propagation, and a unique feature map summation. Supervision is associated with the two-vessel extraction layers using separate loss/sigmoid function. Finally, the weighted fusion of the individual loss/sigmoid function produces the desired blood vessel probability map. It is then binary segmented and validated for performance.

Results

The VSSC Net shows improved accuracy values on the standard retinal and coronary angiogram datasets respectively. The computational time required to segment the blood vessels is 0.2 seconds using GPU. Moreover, the vessel extraction layer uses a lesser parameter count of 0.4 million parameters to accurately segment the blood vessels.

Conclusion

The proposed VSSC Net that segments blood vessels from both the retinal fundus images and coronary angiogram can be used for the early diagnosis of vessel disorders. Moreover, it could aid the physician to analyze the blood vessel structure of images obtained from multiple imaging sources.

中文翻译：

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VSSC Net：用于血管分割的血管专用跳过链卷积网络

背景和目标

深度学习技术有助于开发网络模型，有助于早期诊断威胁生命的疾病。要筛查和诊断视网膜底和冠状血管疾病，最重要的步骤是正确分割血管。

方法

本文旨在在执行图像特定的预处理后，使用单个VSSC网络从冠状动脉血管造影照片和视网膜眼底图像中分割血管。VSSC Net在基础VGG-16网络的顶部使用两容器提取层，并增加了监管。血管提取层包括用于定位血管的特定于血管的卷积块，用于实现丰富特征传播的跳过链卷积层以及唯一的特征图求和。使用单独的损失/ S形函数将监督与两船提取层相关联。最后，单个损失/乙状结肠功能的加权融合产生所需的血管概率图。然后将其二进制分割并验证性能。

结果

VSSC Net分别在标准视网膜和冠状动脉血管造影数据集上显示了更高的准确性值。使用GPU分割血管所需的计算时间为0.2秒。而且，血管提取层使用较少的40万个参数计数来精确地分割血管。

结论

从视网膜眼底图像和冠状动脉血管造影中分割血管的拟议VSSC网络可用于血管疾病的早期诊断。而且，它可以帮助医师分析从多个成像源获得的图像的血管结构。