Zhou Lishi, Wen Haotian, Kuschnerus Inga C, Chang Shery L Y
School of Materials Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Electron Microscope Unit, Mark Wrainwright Analytical Centre, University of New South Wales, Sydney, NSW 2052, Australia.
Nanomaterials (Basel). 2024 Jul 9;14(14):1169. doi: 10.3390/nano14141169.
Morphologies of nanoparticles and aggregates play an important role in their properties for a range of applications. In particular, significant synthesis efforts have been directed toward controlling nanoparticle morphology and aggregation behavior in biomedical applications, as their size and shape have a significant impact on cellular uptake. Among several techniques for morphological characterization, transmission electron microscopy (TEM) can provide direct and accurate characterization of nanoparticle/aggregate morphology details. Nevertheless, manually analyzing a large number of TEM images is still a laborious process. Hence, there has been a surge of interest in employing machine learning methods to analyze nanoparticle size and shape. In order to achieve accurate nanoparticle analysis using machine learning methods, reliable and automated nanoparticle segmentation from TEM images is critical, especially when the nanoparticle image contrast is weak and the background is complex. These challenges are particularly pertinent in biomedical applications. In this work, we demonstrate an efficient, robust, and automated nanoparticle image segmentation method suitable for subsequent machine learning analysis. Our method is robust for noisy, low-electron-dose cryo-TEM images and for TEM cell images with complex, strong-contrast background features. Moreover, our method does not require any a priori training datasets, making it efficient and general. The ability to automatically, reliably, and efficiently segment nanoparticle/aggregate images is critical for advancing precise particle/aggregate control in biomedical applications.
纳米颗粒和聚集体的形态在其一系列应用的性能中起着重要作用。特别是，在生物医学应用中，大量的合成工作致力于控制纳米颗粒的形态和聚集行为，因为它们的尺寸和形状对细胞摄取有重大影响。在几种形态表征技术中，透射电子显微镜（TEM）可以提供纳米颗粒/聚集体形态细节的直接和准确表征。然而，手动分析大量的TEM图像仍然是一个费力的过程。因此，人们对采用机器学习方法来分析纳米颗粒的尺寸和形状兴趣大增。为了使用机器学习方法实现准确的纳米颗粒分析，从TEM图像中进行可靠且自动的纳米颗粒分割至关重要，尤其是当纳米颗粒图像对比度较弱且背景复杂时。这些挑战在生物医学应用中尤为突出。在这项工作中，我们展示了一种适用于后续机器学习分析的高效、稳健且自动的纳米颗粒图像分割方法。我们的方法对于有噪声的低电子剂量冷冻TEM图像以及具有复杂、强对比度背景特征的TEM细胞图像都很稳健。此外，我们的方法不需要任何先验训练数据集，使其高效且通用。自动、可靠且高效地分割纳米颗粒/聚集体图像的能力对于推进生物医学应用中精确的颗粒/聚集体控制至关重要。
