论文标题
基于词典的八八片词血管分割方法
Dictionary-based Method for Vascular Segmentation for OCTA Images
论文作者
论文摘要
光学相干断层扫描（OCTA）是一种成像技术，可以对视网膜中的微脉管系统进行非侵入性研究。 Octa使用激光反射率来测量移动的血细胞。因此，它可视化视网膜中的血流，可用于确定或多或少的血流。八颗图像包含毛细血管网络以及较大的血管，在本文中，我们提出了一种将较大容器，毛细血管和背景分离的方法。分割是使用基于字典的机器学习方法获得的，该方法需要培训数据来学习分割模型的参数。在这里，我们详细说明了该方法如何应用于八片图像，我们演示了它如何稳健地标记毛细血管和血管，从而为量化视网膜血流提供了基础。
Optical coherence tomography angiography (OCTA) is an imaging technique that allows for non-invasive investigation of the microvasculature in the retina. OCTA uses laser light reflectance to measure moving blood cells. Hereby, it visualizes the blood flow in the retina and can be used for determining regions with more or less blood flow. OCTA images contain the capillary network together with larger blood vessels, and in this paper we propose a method that segments larger vessels, capillaries and background. The segmentation is obtained using a dictionary-based machine learning method that requires training data to learn the parameters of the segmentation model. Here, we give a detailed description of how the method is applied to OCTA images, and we demonstrate how it robustly labels capillaries and blood vessels and hereby provides the basis for quantifying retinal blood flow.
