Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

电子显微镜对于检查亚微米尺度固体材料的形态和成分是必不可少的。为了研究材料开发中广泛使用的粉末样品，扫描电子显微镜 (SEM) 越来越多地在实验室规模中使用，以生成包含数百张图像的大型数据集。解析这些图像以识别不同的颗粒并确定其形态需要仔细分析，并且自动化该过程仍然具有挑战性。在这项工作中，我们增强了 Mask R-CNN 架构，以开发一种自动分割 SEM 图像中颗粒的方法。我们解决了测量固有的几个挑战，例如图像模糊和颗粒团聚。此外，当此类问题妨碍粒子的准确分割时，我们的方法可以考虑预测的不确定性。认识到大型数据集中经常存在不同的长度尺度，我们使用这个框架创建两个模型，分别训练它们来处理在低或高放大倍数下获得的图像。通过在各种无机样品上测试这些模型，我们的颗粒分割方法超越了现有的自动分割方法，并产生了与三位领域专家的预测相当的结果，揭示了相当的准确性，同时只需要一小部分时间。这些发现凸显了深度学习在推进材料表征自主工作流程方面的潜力。