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相关概念视频

Overview of Microscopy Techniques01:22

Overview of Microscopy Techniques

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The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...
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Time-of-flight fluorescence depth mapping using a spatiotemporal deep learning model.

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相关实验视频

Updated: Sep 10, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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基于深度学习的内镜单射边投影造型测量

Ruizhi Zuo1, Shuwen Wei1, Yaning Wang1

  • 1Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States.

Journal of biomedical optics
|August 21, 2025
PubMed
概括
此摘要是机器生成的。

一个新的单拍边缘投影特征测量 (FPP) 系统使用深度学习来实时生成深度图. 这种内镜方法提高了手术指导的准确性和速度.

关键词:
三维光学成像深度学习视内镜边缘投射特征测量手术指导

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Conducting Multiple Imaging Modes with One Fluorescence Microscope
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Conducting Multiple Imaging Modes with One Fluorescence Microscope

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Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy
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Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

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相关实验视频

Last Updated: Sep 10, 2025

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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

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Conducting Multiple Imaging Modes with One Fluorescence Microscope
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Conducting Multiple Imaging Modes with One Fluorescence Microscope

Published on: October 28, 2018

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Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy
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Rapid Acquisition of 3D Images Using High-resolution Episcopic Microscopy

Published on: November 21, 2016

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科学领域:

  • 医学成像
  • 机器人技术
  • 计算机视觉

背景情况:

  • 由于多次图像采集,传统的边缘投影造型测量 (FPP) 对于动态手术测量来说太慢了.
  • 高速,准确的深度地图生成对于实时的机器人手术指导至关重要.

研究的目的:

  • 开发和演示基于深度学习的,用于内镜外科指导的一次性FPP系统.
  • 实现目标组织的实时,准确的深度地图生成.

主要方法:

  • 使用双通道内镜设计了一个内镜单射FPP系统.
  • 开发了一个深度学习网络,将MaskNet用于细分和DepthNet用于深度预测.
  • 使用数据合成方法创建多样化的培训数据集.

主要成果:

  • 该系统的最大深度预测误差约为2毫米.
  • 每的处理时间约为12.75毫秒.
  • 对于单射FPP设置,确定了20 Hz的最佳边缘图案频率.

结论:

  • 基于深度学习的一次性FPP内镜系统有效生成毫米级准确的实时深度图.
  • 这项技术有可能显著提高图像引导的机器人手术的可靠性.