Large-scale electron microscopy (EM) datasets generated using (semi-)
automated microscopes are becoming the standard in EM. Given the vast amounts
of data, manual analysis of all data is not feasible, thus automated analysis
is crucial. The main challenges in automated analysis include the annotation
that is needed to analyse and interpret biomedical images, coupled with
achieving high-throughput. Here, we review the current state-of-the-art of
automated computer techniques and major challenges for the analysis of
structures in cellular EM. The advanced computer vision, deep learning and
software tools that have been developed in the last five years for automatic
biomedical image analysis are discussed with respect to annotation,
segmentation and scalability for EM data. Integration of automatic image
acquisition and analysis will allow for high-throughput analysis of
millimeter-range datasets with nanometer resolution.

本文研究了大规模电子显微镜技术下生物医学图像分析的自动化方法和挑战，并讨论了近五年来在自动化人工智能计算机视觉、深度学习和软件工具方面所取得的进步，探讨了自动化图像获取与分析的整合，以实现毫米级纳米分辨率的高通量分析。