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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

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Published on: March 26, 2020

经常性自我融合:用于一致的视网膜OCT细分的代拒绝.

Shuwen Wei¹, Yihao Liu¹, Zhangxing Bian¹

¹Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Ophthalmic medical image analysis : 10th International Workshop, OMIA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings. OMIA (Workshop) (10th : 2023 : Vancouver, B.C. ; Online)

|February 6, 2024

概括

这项研究引入了一种反复的自我融合 (RSF) 算法,以标准化不同光学连贯断层扫描 (OCT) 设备的视网膜层厚度测量. RSF算法提高了细分一致性,以更好地监测疾病.

关键词:

丹尼西斯 (Denoise) 是一种消毒剂.光学连贯性断层扫描技术.分段化分段化分段化分段化

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科学领域:

眼科医生眼科眼科
医疗成像医学成像
生物医学工程生物医学工程

背景情况:

光学连贯断层扫描 (OCT) 在眼科中对视网膜成像至关重要.
不同的OCT设备间不一致的视网膜层厚度测量阻碍了数据比较和纵向研究.
为了可靠的海外国家和地区数据分析,需要标准化.

研究的目的:

引入和验证一个循环自我融合 (RSF) 算法,以协调OCT视网膜层厚度测量.
提高用于疾病检测和监测的OCT图像分析的一致性和可靠性.
为了解决Spectralis和Cirrus OCT设备之间的测量差异.

主要方法:

基于自我融合方法的循环自我融合 (RSF) 算法的开发.
使用RSF算法对视网膜OCT图像进行代性无色化.
基于深度学习的细分算法的应用,用于下游分析.
使用来自Spectralis和Cirrus设备的配对OCT扫描的大数据集进行验证.

主要成果:

RSF算法有效地减少了OCT图像中的斑点对比度.
在视网膜OCT细分中实现了更好的一致性.
该算法证明了不同OCT设备的数据可比性得到了改善.
RSF算法显示了更准确的疾病监测的潜力.

结论:

循环自我融合 (RSF) 算法提供了一个强大的解决方案,用于标准化OCT测量.
这种方法提高了视网膜成像分析在眼科的可靠性.
RSF促进了对视网膜和神经系统疾病的更准确的纵向监测.