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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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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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Three-dimensional Optical-resolution Photoacoustic Microscopy
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无监督的域间转换用于几乎染色的高分辨率中红外光学显微镜,使用可解释的深度学习.

Eunwoo Park1,2, Sampa Misra1,2, Dong Gyu Hwang2,3

  • 1Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.

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概括

我们开发了可解释的深度学习来增强中红外光学显微镜 (MIR-PAM) 图像. 这种方法实现了高分辨率,无标签的细胞成像,克服了传统MIR-PAM的分辨率限制.

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Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke
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科学领域:

  • 生物医学成像技术 生物医学成像技术
  • 光学显微镜的使用方法
  • 计算生物学 计算生物学

背景情况:

  • 中红外光声学显微镜 (MIR-PAM) 提供无标签的生化信息.
  • 与共聚焦光显微镜 (CFM) 相比,MIR-PAM的空间分辨率受到长光波长的限制.
  • 现有的方法缺乏可解释性和图像转换的稳定性.

研究的目的:

  • 开发一个可解释的深度学习 (XDL) 框架,用于将低分辨率的MIR-PAM图像转换为高分辨率,几乎染色图像.
  • 为了提高MIR-PAM的空间分辨率和可解释性.
  • 为了实现无标签,高分辨率的细胞成像.

主要方法:

  • 无监督生成对抗网络 (GAN) 用于域间图像转换.
  • 一个突出性约束被整合到GAN中,以提高可解释性.
  • 在培养的人类心脏纤维细胞上验证了XDL框架,将结果与CFM图像进行比较.

主要成果:

  • XDL框架成功地将低分辨率的MIR-PAM图像转换为高分辨率的,类似于对焦的图像.
  • 该方法准确地识别了细胞核和丝状动蛋白.
  • XDL框架表现出稳定可靠的性能,确保图像域之间具有相似的突出性.

结论:

  • 基于可解释的深度学习的MIR-PAM (XDL-MIR-PAM) 可实现无标签的高分辨率重复细胞成像.
  • 这种技术克服了传统MIR-PAM的空间分辨率限制.
  • 通过提供详细的,无标签的成像,XDL-MIR-PAM为细胞生物学研究提供了显著的好处.