TissueProf:一个ImageJ/斐济插件,用于基于光信号的组织概况.
Emre Düşünceli1, Seiya Yamada1,2, Takashi Namba1,3,4
1Neuroscience Center, HiLIFE - Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
The European journal of neuroscience
|April 3, 2025
概括
研究人员开发了一个用户友好的ImageJ/Fiji插件,用于半自动分析多通道显微镜图像. 这种工具简化了细胞识别和标记器共表达分析,减少了神经科学等领域的工作量.
科学领域:
- 生物医学成像学 生物医学成像学
- 细胞生物学 细胞生物学
- 神经科学是一个神经科学.
背景情况:
- 光免疫组织化学对于分析组织中的细胞群至关重要.
- 高细胞密度和细胞外背景信号对准确的细胞识别和标记者联合表达分析构成挑战.
- 现有的图像分析软件往往缺乏研究人员所需的自动化和灵活性.
研究的目的:
- 开发一个用户友好的ImageJ/Fiji插件,用于半自动分析多通道显微镜图像.
- 为应对在密集组织样本中细胞识别和标记者共表达分析的挑战.
- 为了减少研究人员的实验工作量和时间投资.
主要方法:
- 开发了一个半自动图像分析管道,作为一个ImageJ/Fiji插件实现.
- 该管道包括细胞细分,可选的手动校正和分子协同表达分析.
- 使用深度学习网络进行光信号定位和空间分析,以基于ROI的共同表达量化.
主要成果:
- 该插件基于光信号自动识别感兴趣的区域 (ROI).
- 它量化了表达单个分子和它们的组合的细胞,以及信号强度.
- 输出在Excel文件中提供,详细说明用户定义区域内的单元数和协同表达模式.
结论:
- 开发的插件提供了一种灵活和半自动化的解决方案,用于分析复杂的多通道显微镜图像.
- 它显著减轻了分析组织样本所需的工作量和时间,特别是在神经科学中.
- 这种工具提高了细胞识别和标记者联合表达研究的效率和准确性.
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