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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.
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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Instrument Calibration

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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手术仪器检测算法基于改进的YOLOv7x.

Boping Ran1, Bo Huang1, Shunpan Liang1

  • 1School of Information Science and Engineering, Yanshan University, Qinhuangdao 066000, China.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
概括
此摘要是机器生成的。

这项研究增强了YOLOv7x用于使用计算机视觉精确的手术仪器计数,提高了患者的安全性. 改进的算法在检测仪器方面表现出卓越的性能,即使在具有挑战性的视觉条件下.

关键词:
这就是YOLOV7x.计算机视觉 计算机视觉深度学习是一种深度学习.手术仪器检测仪器手术仪器检测器

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科学领域:

  • 计算机视觉 计算机视觉
  • 医疗成像医学成像
  • 医疗保健中的人工智能

背景情况:

  • 手动的手术仪器计数存在错误的风险,影响患者的安全,并导致医疗纠纷.
  • 现有的计算机视觉方法在各种照明下与密集,封闭或类似形状的手术仪器作斗争.
  • 医疗信息化的进步需要针对手术仪器管理提供强大的自动化解决方案.

研究的目的:

  • 通过使用增强的物体检测算法来提高手术仪器检测的准确性和稳定性.
  • 为了应对仪器阻塞,密集排列,相似外观和不同的照明条件等挑战.
  • 促进更安全的外科手术程序和医疗信息化发展.

主要方法:

  • 修改了YOLOv7x对象检测算法,将RepLK Block模块整合到骨干网络中,以增强形状特征学习.
  • 将ODConv结构集成到部模块中,以提高特征提取能力和上下文信息捕获.
  • 开发并利用OSI26数据集,包括26种外科手术仪器的452张图像,用于模型培训和验证.

主要成果:

  • 改进的YOLOv7x算法实现了高性能指标:F1得分 (94.7%),AP (91.5%),AP50 (99.1%) 和AP75 (98.2%).
  • 与基线算法相比,表现出显著的改进,各自指标的增长率为4.6%,3.1%,3.6%和3.9%.
  • 性能优于其他主流的物体检测算法,表明在手术仪器检测中具有卓越的准确性和稳定性.

结论:

  • 改进的YOLOv7x算法有效地解决了手术仪器检测方面的挑战,提供了更高的准确性和稳定性.
  • 拟议的方法显著改善了手术仪器的识别,有助于提高手术安全和患者健康.
  • 这一进步通过可靠的自动化仪器管理来支持医疗信息化的更广泛目标.