基于去噪机制学习的光学相干断层血管成像中央凹无血管区检测算法

Yih-Cherng Lee, Jian-Jiun Ding, L. Yeung, Tay-Wey Lee, Chia-Jung Chang, Yu-Tze Lin
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引用次数: 0

摘要

中央凹无血管区(FAZ)是视网膜图像上血管稀疏分布的区域。它有助于识别糖尿病视网膜病变。在这项研究中,我们研究了光学相干断层血管造影(OCTA)来更精确地分析FAZ的范围。此外,采用基于学习的去噪结构,可以很好地区分血管和噪声。利用这些技术可以很好地提取OCTA图像中的FAZ,并可以准确地估计FAZ的面积。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algorithm of Foveal Avascular Zone Detection with Denoising Mechanism Learning on Optical Coherence Tomography Angiography Images
The foveal avascular zone (FAZ) is a region on a retinal image where blood vessels distribute sparsely. It helps identify diabetic retinopathy. In this study, we investigate the optical coherence tomography angiography (OCTA) to analyze the extent of the FAZ more precisely. Moreover, a learning-based denoising architecture is applied to well distinguish the vascular vessel and the noise. With these techniques, the FAZ in an OCTA image can be well extracted and its area can be estimated accurately.
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