论文标题
Asocem：冷冻EM中污染的自动分割
ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM
论文作者
论文摘要
粒子采摘目前是冷冻电子显微镜单粒子重建管道中的关键步骤。获得的显微照片中的污染会严重降低粒子采摘器的性能，因此在收集的颗粒堆栈中是许多``非粒子''。在本文中，我们介绍了Asocem（Cryo-Em中污染的自动分割），这是一种自动检测和分段污染的方法，仅作为输入仅作为近似粒径。特别是，它不需要任何参数调整或手动干预。我们的方法基于这样的观察，即受污染区域的统计分布与显微照片的其余部分不同。这种非限制性假设允许自动检测到各种类型的污染物，从支撑网格的碳边缘到不同尺寸的高对比度斑点。我们使用包含各种污染物的各种实验数据集证明了算法的效率。 Asocem作为KLT Picker \ Cite {Eldar2020207473}的一部分集成，可在\ url {https://github.com/shkolniskylab/kltpicker2}上找到。
Particle picking is currently a critical step in the cryo-electron microscopy single particle reconstruction pipeline. Contaminations in the acquired micrographs severely degrade the performance of particle pickers, resulting is many ``non-particles'' in the collected stack of particles. In this paper, we present ASOCEM (Automatic Segmentation Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which requires as an input only the approximated particle size. In particular, it does not require any parameter tuning nor manual intervention. Our method is based on the observation that the statistical distribution of contaminated regions is different from that of the rest of the micrograph. This nonrestrictive assumption allows to automatically detect various types of contaminations, from the carbon edges of the supporting grid to high contrast blobs of different sizes. We demonstrate the efficiency of our algorithm using various experimental data sets containing various types of contaminations. ASOCEM is integrated as part of the KLT picker \cite{ELDAR2020107473} and is available at \url{https://github.com/ShkolniskyLab/kltpicker2}.
