Optical coherence tomography angiography (OCTA) is becoming increasingly popular for neuroscientific study, but it remains challenging to objectively quantify angioarchitectural properties from 3D OCTA images. This is mainly due to projection artifacts or “tails” underneath vessels caused by multiple-scattering, as well as the relatively low signal-to-noise ratio compared to fluorescence-based imaging modalities. Here, we propose a set of deep learning approaches based on convolutional neural networks (CNNs) to automated enhancement, segmentation and gap-correction of OCTA images, especially of those obtained from the rodent cortex. Additionally, we present a strategy for skeletonizing the segmented OCTA and extracting the underlying vascular graph, which enables the quantitative assessment of various angioarchitectural properties, including individual vessel lengths and tortuosity. These tools, including the trained CNNs, are made publicly available as a user-friendly toolbox for researchers to input their OCTA images and subsequently receive the underlying vascular network graph with the associated angioarchitectural properties.

中文翻译：

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深度学习工具箱，用于自动增强，分割和绘制皮层光学相干断层扫描微血管造影图

光学相干断层扫描血管造影（OCTA）在神经科学研究中正变得越来越流行，但是从3D OCTA图像中客观量化血管架构特性仍然具有挑战性。这主要是由于多重散射引起的血管下方的投影伪影或“尾巴”，以及与基于荧光的成像方式相比信噪比相对较低。在这里，我们提出了一套基于卷积神经网络（CNN）的深度学习方法，以自动增强，分割和间隙校正OCTA图像，尤其是从啮齿动物皮层获得的图像。此外，我们提出了一种策略，可用于对分割的OCTA进行骨架化并提取基本的血管图，从而可以定量评估各种血管架构特性，包括个别船只的长度和曲折度。这些工具，包括训练有素的CNN，可作为用户友好的工具箱公开提供给研究人员，以输入他们的OCTA图像，然后接收具有相关血管架构特性的基础血管网络图。