SEM获取和特征跟踪的工作流自动化。

IF 2.1 3区 工程技术 Q2 MICROSCOPY Ultramicroscopy Pub Date : 2025-03-01 Epub Date: 2024-12-08 DOI:10.1016/j.ultramic.2024.114093
Sabrina Clusiau, Nicolas Piché, Nicolas Brodusch, Mike Strauss, Raynald Gauvin
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引用次数: 0

摘要

使用扫描电子显微镜(sem)获取多个高倍率,高分辨率的图像进行定量分析是一项耗时且重复的任务。我们提出了一种自动化扫描电镜图像采集的工作流程,并演示了其在纳米颗粒(NP)分析中的应用。获取该类型标本的多个图像是必要的,以获得NP种群的完整和适当的特征,并获得具有统计学代表性的结果。事实上,一张高倍图像只扫描一小块样本区域,只包含少量np。所提出的工作流程成功地应用于使用基于Python的脚本在样本的同一区域上以三种不同的放大倍数(20,000倍,60,000倍和200,000倍)从图像蒙太奇中获得大小分布。自动化工作流程包括电子束的顺序重新定位、相邻图像的拼接、特征分割和NP大小计算。结果表明,NPs在较高的放大倍率下表现最好,因为较低的放大倍率受到其像素大小的限制。在高放大倍率下,特征表征的准确性提高,突出了自动化的重要性:在低放大倍率下,许多高放大倍率的采集需要覆盖样品的类似区域。因此,我们也提出了智能波束定位的特征跟踪,作为盲目获取超大图像阵列的替代方案。特征跟踪是通过集成显微镜任务和图像处理任务来实现的,只有感兴趣的区域才会以高分辨率成像,从而减少了总采集时间。
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Workflow automation of SEM acquisitions and feature tracking.

Acquiring multiple high magnification, high resolution images with scanning electron microscopes (SEMs) for quantitative analysis is a time consuming and repetitive task for microscopists. We propose a workflow to automate SEM image acquisition and demonstrate its use in the context of nanoparticle (NP) analysis. Acquiring multiple images of this type of specimen is necessary to obtain a complete and proper characterization of the NP population and obtain statistically representative results. Indeed, a single high magnification image only scans a small area of sample, containing only few NPs. The proposed workflow is successfully applied to obtain size distributions from image montages at three different magnifications (20,000x, 60,000x and 200,000x) on the same area of the sample using a Python based script. The automated workflow consists of sequential repositioning of the electron beam, stitching of adjacent images, feature segmentation, and NP size computation. Results show that NPs are best characterized at higher magnifications, since lower magnifications are limited by their pixel size. Increased accuracy of feature characterization at high magnification highlights the importance of automation: many high-magnification acquisitions are required to cover a similar area of the sample at low magnification. Therefore, we also present feature tracking with smart beam positioning as an alternative to blind acquisition of very large image arrays. Feature tracking is achieved by integrating microscope tasks with image processing tasks, and only areas of interest will be imaged at high resolution, reducing total acquisition duration.

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来源期刊
Ultramicroscopy
Ultramicroscopy 工程技术-显微镜技术
CiteScore
4.60
自引率
13.60%
发文量
117
审稿时长
5.3 months
期刊介绍: Ultramicroscopy is an established journal that provides a forum for the publication of original research papers, invited reviews and rapid communications. The scope of Ultramicroscopy is to describe advances in instrumentation, methods and theory related to all modes of microscopical imaging, diffraction and spectroscopy in the life and physical sciences.
期刊最新文献
Aberration calculation of microlens array using differential algebraic method. Relativistic EELS scattering cross-sections for microanalysis based on Dirac solutions. Improved precision and accuracy of electron energy-loss spectroscopy quantification via fine structure fitting with constrained optimization. Workflow automation of SEM acquisitions and feature tracking. Enhancing subsurface imaging in ultrasonic atomic force microscopy with optimized contact force.
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