深度强化学习用于使用OCT图像强度的自动失焦校正
Guozheng Xu1, Thomas J Smart2, Arman Athwal1
1Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, United Kingdom.
Biomedical optics express
|October 20, 2025
概括
本研究引入了一种深度强化学习方法,通过在成像过程中纠正轴向运动和焦点来稳定视网膜光学连贯断层扫描 (OCT) 图像. 这种自动化技术提高了图像清晰度,以更好地可视化视网膜层.
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:
- 光学连贯断层扫描 (OCT) 对视网膜的成像容易受到受试者运动和焦点变化的不稳定.
- 轴运动和眼部适应引起的失焦会降低图像质量,影响视网膜层和侧面分辨率的可视化.
- 当前的方法可能无法充分解决实时OCT视网膜成像中的轴运动和焦点相关的工件.
研究的目的:
- 开发和验证用于稳定轴向运动和OCT视网膜成像中的焦点的自动化程序.
- 实施深度强化学习 (DRL) 方法,使用B扫描图像进行实时失焦校正.
- 提高OCT视网膜成像诊断目的的清晰度和可靠性.
主要方法:
- 开发了一种使用深度强化学习 (DRL) 的自动化程序,用于同时稳定轴运动和焦点.
- DRL模型使用in silico数据进行训练,并通过in vivo实验进行微调.
- 校正方法只需要B扫描图像作为输入,从而实现实时应用.
主要成果:
- 基于DRL的程序有效地稳定了OCT视网膜图像的轴向运动和失焦.
- 实现了实时校正,显著提高了视网膜截面和面部可视化的质量.
- 通过in silico和in vivo实验的验证证实了该程序的性能.
结论:
- 本文介绍的自动化DRL程序为提高OCT视网膜图像稳定性提供了一个强大的解决方案.
- 这种方法有可能通过提供更清晰的视网膜图像来提高眼科诊断的准确性.
- 基于B扫描的实时校正适用于OCT视网膜成像中的临床应用.
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