Mohsin Abu S M, Choudhury Shadab H
Nanotechnology, IoT and Applied Machine Learning Research Group, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh.
ACS Omega. 2025 Jan 3;10(1):862-870. doi: 10.1021/acsomega.4c07914. eCollection 2025 Jan 14.
Nanoparticles embedded in polymer matrices play a critical role in enhancing the properties and functionalities of composite materials. Detecting and quantifying nanoparticles from optical images (fixed samples-in vitro imaging) is crucial for understanding their distribution, aggregation, and interactions, which can lead to advancements in nanotechnology, materials science, and biomedical research. In this article, we propose an ensembled deep learning approach for automatic nanoparticle detection and oligomerization quantification in a polymer matrix for optical images. The majority of prior studies of nanoparticle identification and categorization of fixed samples are based on scanning electron microscopy (SEM) or transmission electron microscopy (TEM) images, which are destructive to biological imaging. However, the proposed study is based on optical images, which are susceptible to noise, low contrast, anisotropic shape, overlapping of the point spread function, plasmon coupling, and resolution limitations. In this study, we fine-tune a deep neural network architecture, YOLOv8, on a carefully annotated data set of correlated optical and SEM images of 80 nm gold nanospheres (AuNSs) of varying oligomerization states. The resultant model features a weighted average accuracy of 80.7% for quantification of AuNSs and determination of their oligomeric state, far surpassing the capabilities of existing manual image processing methods. We also demonstrate its speed and effectiveness in nanoparticle detection and oligomerization within the polymer matrix through tests on high-density uncorrelated optical images. The optical image-based quantification technique will be useful for (live samples-for in vivo imaging) analyzing nanoparticle uptake, oligomerization state, and aggregation kinetics in live cells and identifying stoichiometry of membrane protein and its interactions, nanoparticle-cell interaction, cell signaling imaging, and drug delivery.
嵌入聚合物基质中的纳米颗粒在增强复合材料的性能和功能方面起着关键作用。从光学图像（固定样本 - 体外成像）中检测和量化纳米颗粒对于理解它们的分布、聚集和相互作用至关重要，这可以推动纳米技术、材料科学和生物医学研究的发展。在本文中，我们提出了一种集成深度学习方法，用于对聚合物基质中的光学图像进行自动纳米颗粒检测和低聚物量化。先前大多数关于固定样本中纳米颗粒识别和分类的研究是基于扫描电子显微镜（SEM）或透射电子显微镜（TEM）图像，这些方法对生物成像具有破坏性。然而，本研究所基于的光学图像容易受到噪声、低对比度、各向异性形状、点扩散函数重叠、等离子体耦合和分辨率限制的影响。在本研究中，我们在一个精心注释的数据集上对深度神经网络架构YOLOv8进行了微调，该数据集包含不同低聚状态的80纳米金纳米球（AuNSs）的相关光学图像和SEM图像。所得模型在量化AuNSs及其低聚状态方面的加权平均准确率为80.7%，远远超过了现有的手动图像处理方法的能力。我们还通过对高密度不相关光学图像的测试，展示了其在聚合物基质中纳米颗粒检测和低聚方面的速度和有效性。基于光学图像的量化技术将有助于（活样本 - 体内成像）分析活细胞中纳米颗粒的摄取、低聚状态和聚集动力学，以及识别膜蛋白的化学计量及其相互作用、纳米颗粒 - 细胞相互作用、细胞信号成像和药物递送。
