The quantification of blood flow velocity in the human conjunctiva is clinically essential for assessing microvascular hemodynamics. Since the conjunctival microvessel is imaged in several seconds, eye motion during image acquisition causes motion artifacts limiting the accuracy of image segmentation performance and measurement of the blood flow velocity. In this paper, we introduce a novel customized optical imaging system for human conjunctiva with deep learning-based segmentation and motion correction. The image segmentation process is performed by the Attention-UNet structure to achieve high-performance segmentation results in conjunctiva images with motion blur. Motion correction processes with two steps—registration and template matching—are used to correct for large displacements and fine movements. The image displacement values decrease to 4–7 μm during registration (first step) and less than 1 μm during template matching (second step). With the corrected images, the blood flow velocity is calculated for selected vessels considering temporal signal variances and vessel lengths. These methods for resolving motion artifacts contribute insights into studies quantifying the hemodynamics of the conjunctiva, as well as other tissues.

中文翻译：

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基于深度学习的稳定算法对人结膜微血管血流速度的量化

在人体结膜中血流速度的量化对于评估微血管血流动力学是临床必不可少的。由于结膜微血管在几秒钟内成像，因此在图像采集过程中的眼球运动会导致运动伪影，从而限制了图像分割性能和血流速度测量的准确性。在本文中，我们介绍了一种用于人体结膜的新型定制光学成像系统，该系统具有基于深度学习的分割和运动校正功能。图像分割过程由Attention-UNet结构执行，以在具有运动模糊的结膜图像中实现高性能的分割结果。具有两个步骤的运动校正过程（套准和模板匹配）用于校正大位移和精细运动。在配准（第一步）过程中，图像位移值减小到4–7μm，在模板匹配（第二步）过程中，图像位移值减小至小于1μm。利用校正的图像，考虑时间信号变化和血管长度，为选定的血管计算血流速度。这些用于解决运动伪影的方法有助于定量研究结膜及其他组织的血液动力学。