论文标题
从高分辨率传输电子显微镜数据中进行分割和缺陷识别的机器学习管道
Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data
论文作者
论文摘要
在传输电子显微镜领域，数据解释通常滞后于采集方法，因为图像处理方法通常必须在单个数据集中手动量身定制。机器学习提供了一种有希望的方法，可快速，准确地分析电子显微镜数据。在这里，我们演示了一条灵活的两步管道，用于分析高分辨率透射电子显微镜数据，该数据使用U-NET进行分割，然后是一个随机森林来检测堆叠断层。我们训练有素的U-NET能够从无定形背景的纳米粒子区域分割骰子系数为0.8，并且明显优于传统图像分割方法。然后，使用这些分段区域，我们能够对纳米颗粒是否包含具有86％精度的可见堆叠断层进行分类。我们为社区提供此适应性管道作为开源工具。分割网络和分类器的组合输出提供了一种方法来确定感兴趣特征的统计分布，例如大小，形状和缺陷的存在，从而可以检测这些特征之间的相关性。
In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two step pipeline for analysis of high resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape and defect presence, enabling detection of correlations between these features.
