合成的OCT-A血管地图使用 fundus 图像和生成对抗网络
Ivan Coronado1, Samiksha Pachade1, Emanuele Trucco2
1McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Scientific reports
|September 15, 2023
概括
这项研究引入了一种使用生成对抗网络自动创建视网膜血管图的新方法,从 fundus 图像中自动创建视网膜血管图,减少手工细分的需要,并可能降低视网膜疾病研究的成本.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在 fundus 图像中血管细分对于理解视网膜疾病和开发生物标志物至关重要.
- 手动细分是耗时的,虽然OCT-A提供了直接的视网膜血管估计,但它有成本和视野等局限性.
- 当前的自动化方法往往需要广泛的手动细分培训.
研究的目的:
- 开发一种自动化策略,使用生成对抗网络 (GAN) 从 fundus 图像创建血管地图.
- 通过手动细分和OCT-A数据训练的最先进的算法来评估拟议的方法.
- 为了评估自动血管细分和血管密度估计的性能,一个关键的OCT-A生物标志物.
主要方法:
- 使用生成对抗网络 (GAN) 来从 fundus 图像中生成血管图,而无需手动分段训练.
- 应用后处理技术,类似于标准面对OCT-A,以完善细分.
- 将基于GAN的方法与使用手动细分图和OCT-A衍生细分的现有算法进行了比较,用于培训和评估.
主要成果:
- 拟议的方法,使用OCT-A作为目标进行训练,在光盘区域产生了改进的血管图.
- 黄斑区域的性能与现有的最好的血管细分算法相美.
- 这种方法被证明是作为船舶密度估计器的有效性,这是OCT-A研究中常见的生物标志物.
结论:
- 这种新的技术显著降低了与训练船分段算法相关的成本和精力.
- 该方法提供了一个可行的替代方案,可以从 fundus 图像中生成准确的血管图.
- 该数据集的公开发布旨在进一步刺激视网膜图像自动化分析的研究.
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