深度学习驱动的自适应光学用于单分子定位显微镜
Peiyi Zhang1, Donghan Ma1,2, Xi Cheng3,4
1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
Nature methods
|September 28, 2023
概括
深度学习纠正生物组织中的异常,以获得更清晰的单分子局部化显微镜 (SMLM) 成像. 这种新的自适应光学方法提高了厚组织样本的分辨率和真实性.
科学领域:
- 生物物理学的生物物理.
- 显微镜的使用方法
- 光学工程是指光学工程.
背景情况:
- 生物组织散射光线,扭曲单分子定位显微镜 (SMLM) 图像并限制分辨率.
- 传统的自适应光学方法在复杂的组织环境中与不一致的反应作斗争.
研究的目的:
- 为SMLM开发基于深度学习的自适应光学系统,直接补偿组织诱导的偏差.
- 为了提高SMLM在厚厚的生物样本中的分辨率和图像准确度.
主要方法:
- 一个深度神经网络被训练,从单分子发射模式推断波面扭曲.
- 该系统使用动态波器和可变形镜来实现实时偏差补偿.
- 该方法在脑组织样本的3D SMLM上进行了测试.
主要成果:
- 深度学习方法直接推断并补偿波面扭曲.
- 该系统成功地估计和纠正了28个波面变形形状.
- 在 >130μm厚的大脑组织中,在分辨率和真实性方面取得了显著的改进.
结论:
- 深度学习驱动的自适应光学为SMLM中的偏差校正提供了强大的解决方案.
- 这种方法克服了传统适应光学在生物组织中的局限性.
- 能够使高分辨率的3D SMLM成像更深入到分散的生物样本中.
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