Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

1.8K
Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
1.8K
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

907
Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
907

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

TOPS-CRISPR: Thermally-regulated and oligonucleotide-mediated one-pot CRISPR-Cas12a assay for ultra-sensitive and rapid on-site diagnostics.

Biosensors & bioelectronics·2026
Same author

Correlation between tumor mutational burden and CT radiographic features in EGFR exon 19 deletion-mutated lung adenocarcinoma: a diagnostic accuracy study.

Frontiers in medicine·2026
Same author

NNMT and the methylation sink: integrating metabolism, epigenetics and immunity in cancer.

BMC medicine·2026
Same author

Successful treatment of tuberculous wounds using Maggot Debridement Therapy (MDT): A case report.

IDCases·2026
Same author

Molecular mechanisms of sudden unexplained death and recent updates on molecular autopsy strategies for forensic investigation.

International journal of legal medicine·2026
Same author

Motion-aware imaging flow cytometry framework for robust counting and tracking of cells with non-uniform velocities in microfluidics.

Applied optics·2026

相关实验视频

Updated: May 3, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.5K

深度μStitch:一个无监督的基于滑动窗的深度全球显微镜图像拼接框架.

Shouyu Wang, Wei Yu

    Applied optics
    |September 22, 2025
    PubMed
    概括

    深度μStitch是一个新的深度学习框架,用于拼接显微镜图像,克服现有方法的局限性. 这种方法提高了全幻灯片成像的视野 (FoV) 拼接质量,使用最小的工件.

    科学领域:

    • 显微镜的使用方法
    • 计算成像技术的成像
    • 生物图像分析 生物图像分析

    背景情况:

    • 显微镜对于生物研究至关重要,但受到小视野 (FoV) 的限制.
    • 扩展FoV的现有方法 (例如全息,图形,多摄像头阵列) 有缺点,如低分辨率,长时间的重建时间或复杂的设置.
    • 当前的FoV拼接技术往往会产生文物,并且在低对比度图像上表现不佳.

    研究的目的:

    • 开发一种先进的无监督深度学习框架,用于拼接显微镜图像.
    • 为了提高整个幻灯片成像的质量,速度和成本效益.
    • 解决现有的FoV拼接方法的局限性,特别是工件生成和低对比度图像处理.

    主要方法:

    • 开发了Deep μStitch,这是一个无监督的,基于滑动窗口的深度全球显微镜图像拼接框架.
    • 在明亮场和光显微镜中应用Deep μStitch.
    • 对已建立的拼接工具进行了比较分析:显微镜图像拼接工具 (MIST),网格拼接和规模不变特征转换 (SIFT).

    主要成果:

    • 深度μStitch在不同的显微镜类型中展示了高的FoV接质量.
    • 该框架在比较分析中实现了几乎最高的峰值信号噪声比 (PSNR) 和结构相似性指数 (SSIM).

    更多相关视频

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    10.6K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K

    相关实验视频

    Last Updated: May 3, 2026

    High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
    09:31

    High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

    Published on: April 28, 2022

    3.5K
    Deep Learning-Based Segmentation of Cryo-Electron Tomograms
    10:25

    Deep Learning-Based Segmentation of Cryo-Electron Tomograms

    Published on: November 11, 2022

    10.6K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.3K
  • 与其他方法相比,Deep μStitch显著减少了可见的工件.

    • 结论:

    • 深度μStitch提供了一个最佳的解决方案,以平衡显微镜图像拼接中的空间分辨率,速度和成本.
    • 该框架为整个幻灯片成像应用提供了一个有希望的进步.
    • 深度μStitch有效地克服了常见的工件和低性能问题与低对比度图像在FoV接相关.