Automated and semi-automated techniques in biomedical electron microscopy (EM) enable the acquisition of large datasets at a high rate. Segmentation methods are therefore essential to analyze and interpret these large volumes of data, which can no longer completely be labeled manually. In recent years, deep learning algorithms achieved impressive results in both pixel-level labeling (semantic segmentation) and the labeling of separate instances of the same class (instance segmentation). In this review, we examine how these algorithms were adapted to the task of segmenting cellular and sub-cellular structures in EM images. The special challenges posed by such images and the network architectures that overcame some of them are described. Moreover, a thorough overview is also provided on the notable datasets that contributed to the proliferation of deep learning in EM. Finally, an outlook of current trends and future prospects of EM segmentation is given, especially in the area of label-free learning.

翻译：生物医学电子显微镜中的自动化和半自动化技术能够以高速率获取大规模数据集。因此，分割方法对于分析和解读这些无法再完全手工标注的大量数据至关重要。近年来，深度学习算法在像素级标注（语义分割）和同一类别中不同实例的标注（实例分割）方面取得了显著成果。本文回顾了这些算法如何被应用于电子显微镜图像中细胞和亚细胞结构的分割任务。我们描述了这类图像带来的特殊挑战以及克服其中部分挑战的网络架构。此外，本文还对推动电子显微镜领域深度学习发展的显著数据集进行了全面概述。最后，对电子显微镜分割的当前趋势和未来前景，尤其是在免标注学习领域，进行了展望。