使用深度学习从无关联的光学图像中量化纳米颗粒的单体-模态分布
Abu S M Mohsin1, Shadab H Choudhury1
1Nanotechnology, IoT and Applied Machine Learning Research Group, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh.
ACS omega
|January 20, 2025
概括
我们使用YOLOv8开发了一个深度学习模型,用于在光学图像中检测和量化纳米粒子. 这种方法准确地分析了聚合物矩阵中的纳米粒子寡合化,这对于材料科学和生物医学应用至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 生物医学研究生物医学研究
背景情况:
- 聚合物矩阵中的纳米粒子增强复合材料的性能.
- 精确检测和量化纳米粒子对于理解它们的行为至关重要.
- 现有的方法通常依赖于破坏性成像技术,如SEM/TEM.
研究的目的:
- 开发一种用于光学图像中纳米粒子检测和寡合化量化的自动深度学习方法.
- 为了克服光学成像的局限性,例如噪音和低对比度.
- 为纳米粒子分析提供SEM/TEM的非破坏性替代品.
主要方法:
- 微调了YOLOv8深度神经网络架构.
- 使用金纳米球 (AuNSs) 的相关光学和SEM图像的数据集.
- 关于注释图像的培训,以识别AuNS及其寡头状态.
主要成果:
- 对于AuNS量化和寡合化状态的确定,达到80.7%的加权平均准确率.
- 与手动图像处理方法相比,证明了卓越的性能.
- 在高密度光学图像上验证了速度和有效性.
结论:
- 拟议的基于光学图像的方法提供了准确和高效的纳米粒子分析.
- 这种技术适用于活体样本的体内成像,使得纳米粒子吸收和相互作用的研究成为可能.
- 潜在的应用包括细胞信号传递,药物输送和理解纳米粒子-细胞相互作用.
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