CABaNe,一个自动化,高吞吐量ImageJ宏用于细胞和神经细胞分析
Nathan Thibieroz1, Fabrice Cordelières2, Paul Machillot1
1Univ Grenoble Alpes , CEA, INSERM U1292 Biosanté , CNRS EMR BRM 5000, Grenoble, France.
eNeuro
|January 30, 2026
概括
我们开发了CABaNe,一个开源的Image J宏,用于在神经生物学研究中自动化高吞吐量神经元长度分析. 与手工方法相比,这种工具提高了精度和速度,有助于研究神经元发育和疾病.
科学领域:
- 神经生物学 神经生物学 神经生物学
- 细胞生物学 细胞生物学
- 图像分析 图像分析
背景情况:
- 神经元长度是理解神经元生长,分化和功能的一个关键参数.
- 现有的神经元长度测量方法通常是手动的,耗时的,容易产生操作者偏见.
- 对于精确的神经元分析的自动化,高通量工具有很大的需求.
研究的目的:
- 介绍CABaNe,一个开源的,基于规则的Image J宏用于自动化高通量细胞分析,重点是神经元长度测量.
- 为了评估CABaNe的性能与手动和辅助测量技术相比.
- 为各种神经生物学分析和潜在的深度学习模型培训提供一个多功能平台.
主要方法:
- 开发CABaNe,一个基于规则的Image J宏,具有图形界面和验证功能.
- 在N2A小鼠神经母细胞瘤细胞上测试基于规则和基于机器学习的细胞识别方法.
- 在小型和大型数据集上对CABaNe与手动和辅助测量技术的性能进行比较分析.
主要成果:
- 与基于机器学习的方法相比,基于规则的细胞识别显示出更高的精度和适应性.
- CABaNe显著减少了小型数据集的分析时间,同时保持或提高了精度.
- 对大型数据集的自动化分析成功地确定了实验条件之间的差异.
结论:
- 在神经生物学中,CABaNe是用于高通量神经元长度和其他细胞参数分析的可行和高效的解决方案.
- 由于CABaNe的开源性质,它可以适应各种研究需求和未来的发展,包括训练深度学习模型.
- CABaNe为手工测量提供了一个强大的,自动化的替代方案,减少偏差并增加神经生物学研究的吞吐量.
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