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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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相关实验视频

Updated: Jul 11, 2025

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
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通过嵌入式神经网络控制用于显微镜的通用自适应光学.

Qi Hu1, Martin Hailstone2, Jingyu Wang1

  • 1Department of Engineering Science, University of Oxford, Oxford, UK.

Light, science & applications
|November 12, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了一种新的机器学习自适应光学 (AO) 方法,用于在显微镜中更快,更灵活的偏差校正. 这种基于物理的方法可以跨不同类型的显微镜进行转移,改善图像质量并提供物理洞察力.

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科学领域:

  • 光学显微镜是一种光学显微镜.
  • 生物医学成像学 生物医学成像学
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 显微镜成像分辨率和对比度受到光学偏差的限制.
  • 现有的自适应光学 (AO) 解决方案往往是特定于模式的.
  • 需要一个适用于各种显微镜的通用AO系统.

研究的目的:

  • 为显微镜开发一种通用且快速的偏差校正方法.
  • 为了创建一个基于物理的,机器学习辅助的波无传感器AO控制 (MLAO) 系统.
  • 为了使AO在不同的显微镜模式中具有可转移性.

主要方法:

  • 实施了一种基于物理的机器学习辅助波浪前线无传感器AO控制 (MLAO) 方法.
  • 设计了一种基于对图像形成的物理理解的新型神经网络 (NN) 架构.
  • 在双光子,三光子和广场3D结构化照明显微镜的控制循环中嵌入了NN.

主要成果:

  • MLAO方法显示出比传统的基于模态的无传感器AO更快,更有效的偏差校正.
  • 成功地将该方法应用于各种显微镜技术,包括两光子,三光子和广场3D结构化照明.
  • 在3D结构,样本运动,低信号噪声比和光波动等具有挑战性的条件下展示了强度.

结论:

  • 开发的MLAO方法为显微镜中偏差校正提供了一个通用的,可翻译的解决方案.
  • 基于物理学的NN提供了物理洞察力,超越了"黑子"的方法.
  • 这种方法提高了复杂的生物成像场景中的图像质量和稳定性.