School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Comput Methods Programs Biomed. 2021 Jan;198:105769. doi: 10.1016/j.cmpb.2020.105769. Epub 2020 Sep 28.
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.
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.
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.
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.
深度学习技术在开发有助于危及生命的疾病早期诊断的网络模型方面发挥了重要作用。为了筛选和诊断视网膜眼底和冠状动脉血管疾病，最重要的步骤是对血管进行适当的分割。
本文旨在使用单个 VSSC Net 对冠状动脉造影和视网膜眼底图像进行血管分割，在对图像进行特定的预处理后进行。VSSC Net 使用双血管提取层，并在基础 VGG-16 网络之上添加了监督。血管提取层包括血管特定的卷积块，以定位血管、跳过链卷积层以实现丰富的特征传播，以及独特的特征图求和。监督与双血管提取层相关联，使用单独的损失/ sigmoid 函数。最后，通过单独的损失/sigmoid 函数的加权融合产生所需的血管概率图。然后对其进行二进制分割并验证性能。
VSSC Net 在标准视网膜和冠状动脉造影数据集上的准确度值都有所提高。使用 GPU 分割血管所需的计算时间为 0.2 秒。此外，血管提取层使用较少的参数计数 0.4 万个参数，可准确地分割血管。
本文提出的从视网膜眼底图像和冠状动脉造影中分割血管的 VSSC Net 可用于血管疾病的早期诊断。此外，它可以帮助医生分析从多种成像源获得的图像的血管结构。
