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基于U2-Net和ResNet50的自动管道用于细菌殖民地计数.

Libo Cao1, Liping Zeng2, Yaoxuan Wang1

  • 1Center for Global Health, Nanjing Medical University, Nanjing 211166, China.

Microorganisms
|January 23, 2024
PubMed
概括

本研究引入了一种自动化系统,用于使用先进的图像处理和卷积神经网络 (CNN) 来计算微生物殖民地. 这种新方法在殖民地计数和粘附分类方面实现了高精度,提高了实验室的效率.

关键词:
在ResNet50中使用ResNet50在U2-Net中使用U2-Net.细菌殖民地的计数.卷积神经网络是一种卷积神经网络.图像分割 图像细分 图像细分图像空间规范化的图像照明强度校正 校正光强度的校正

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科学领域:

  • 微生物学 微生物学
  • 计算机视觉 计算机视觉
  • 生物信息学是一种生物信息学.

背景情况:

  • 准确的微生物殖民地计数对于各种实验室应用至关重要.
  • 手动计数是劳动密集型,容易出错,耗时.
  • 自动化系统可以提高微生物量化中的效率和可重复性.

研究的目的:

  • 利用改进的图像预处理和卷积神经网络 (CNN) 的帮助,开发一个自动化殖民地计数系统.
  • 提高实验室培养中识别和计数单个和多个殖民地目标的准确性和自动化.
  • 为了验证系统在殖民地计数和粘附分类中的性能.

主要方法:

  • 一个LED背光照明照明平台被用于阿加板培养物的图像采集.
  • 基于光强度校正的图像预处理算法被实施,以实现更清晰的殖民地媒介差异化.
  • 使用U2-Net模型对Petri盘边缘和殖民地区域进行细分.
  • 使用ResNet50进行细分殖民地组件的最终自动计数.

主要成果:

  • 对于彼得里盘边缘检测的U2网络,F1得分达到99.5%,MAE为0.0033.
  • 殖民地地区细分的U2-Net获得了96.5%的F1得分和0.005.5%的MAE.
  • 总体殖民地计数恢复率为97.82%,在粘附分类中表现出色.

结论:

  • 开发的自动化系统展示了微生物殖民地计数的高精度和自动化.
  • 拟议的管道,整合光强度校正,U2-Net细分和ResNet50计数,是一种新的方法.
  • 该系统为定量微生物学研究和诊断提供了重大进展.