We present a method for the unsupervised segmentation of electron microscopy images, which are powerful descriptors of materials and chemical systems. Images are oversegmented into overlapping chips, and similarity graphs are generated from embeddings extracted from a domain$\unicode{x2010}$pretrained convolutional neural network (CNN). The Louvain method for community detection is then applied to perform segmentation. The graph representation provides an intuitive way of presenting the relationship between chips and communities. We demonstrate our method to track irradiation$\unicode{x2010}$induced amorphous fronts in thin films used for catalysis and electronics. This method has potential for "on$\unicode{x2010}$the$\unicode{x2010}$fly" segmentation to guide emerging automated electron microscopes.

翻译：我们提出了一种电子显微镜图像的无监督分割方法，这类图像是材料和化学体系的重要表征工具。该方法将图像过分割为重叠的图像块，从领域预训练卷积神经网络提取的嵌入特征构建相似度图，随后采用社区检测中的Louvain方法执行分割。这种图表示方法为图像块与社区之间的关联提供了直观呈现方式。我们通过追踪催化与电子领域薄膜材料中辐照诱导的非晶前沿实例，验证了该方法的有效性。本方法具备对新兴自动化电子显微镜进行“即时”分割来指导操作的潜力。