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相关概念视频

Scanning Electron Microscopy01:07

Scanning Electron Microscopy

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A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
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Preparation of Samples for Electron Microscopy01:20

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To be visualized by an electron microscope, either transmission or scanning, biological samples need to be fixed (stabilized) so the electron beam does not destroy them and dried thoroughly (desiccated/dehydrated) so the vacuum does not affect them. Fixation needs to be done as quickly as possible because the sample properties will start changing as soon as it is removed from its natural environment. For example, in a tissue sample, the oxygen levels begin decreasing, causing an altered...
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相关实验视频

Updated: Jan 17, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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利用未标记的SEM数据集与自我监督的学习来增强粒子细分.

Luca Rettenberger1, Nathan J Szymanski2, Andrea Giunto3

  • 1Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.

npj computational materials
|September 25, 2025
PubMed
概括

使用ConvNeXtV2模型的自我监督学习 (SSL) 显著改善了扫描电子显微镜 (SEM) 图像中的粒子检测. 这种自动化将分析错误减少多达34%,加速材料科学发现.

关键词:
电池 电池 电池 电池 电池计算方法 计算方法工程 工程师 工程师 工程师用于能源和催化物的材料.材料科学是一种材料科学.结构性质 结构性质合成和加工过程中的合成和加工.理论和计算理论和计算

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 扫描电子显微镜 (SEM) 产生了大量的图像数据,需要广泛的用户分析.
  • 自动化SEM图像分析对于实验科学的效率至关重要.
  • 机器学习 (ML),特别是监督学习,是有效的,但受到手动注释需求的阻碍.

研究的目的:

  • 评估自主监督学习 (SSL) 技术用于自动化SEM图像分析.
  • 引入和评估使用ConvNeXtV2架构用于粒子检测的新型SSL方法.
  • 为实用应用提供对数据集大小对SSL性能影响的见解.

主要方法:

  • 在SEM图像数据上开发一个框架来评估SSL技术.
  • 利用ConvNeXtV2架构在SEM图像中进行粒子检测.
  • 策划一个 25,000 SEM 图像的数据集,用于对 SSL 方法进行基准测试.
  • 对数据集大小和SSL性能进行了废弃研究.

主要成果:

  • 基于ConvNeXtV2的SSL模型在各种尺度上展示了卓越的粒子检测性能.
  • 与现有的SSL方法相比,相对错误减少了多达34%.
  • 确定了数据集大小和SSL模型性能之间的关键关系.

结论:

  • 特别是在ConvNeXtV2中,SSL为SEM图像分析提供了监督学习的强大替代方案.
  • 拟议的框架和发现有助于将SSL集成到自主分析管道中.
  • 这项研究通过增强自动图像分析能力来加速材料科学发现.