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.
翻译:使用(半)自动显微镜生成的大型电子显微镜数据集正在成为EM的标准。鉴于数据数量庞大,对所有数据进行人工分析是不可行的,因此自动分析至关重要。自动化分析的主要挑战包括分析和解释生物医学图像所需的说明,以及实现高通量。这里,我们审查目前自动化计算机技术的最新水平和对蜂窝式EM结构分析的主要挑战。在过去五年中开发的用于自动生物医学图像分析的先进计算机视觉、深层学习和软件工具,在批注、分解和EM数据可缩放方面进行了讨论。将自动图像获取和分析结合起来,将能够对毫米距离数据集和纳米分辨率进行高通量分析。