ASOCEM：冷冻EM中污染物的自动分割
ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM
摘要：
粒子拾取是目前低温电子显微镜单粒子重建流水线中的关键步骤。所获得的显微照片中的污染物严重降低了颗粒拾取器的性能，导致在所收集的颗粒堆中存在许多"非颗粒“。在本文中，我们提出了ASOCEM（自动分割的污染物在冷冻EM），自动方法来检测和分割污染物，它只需要作为输入的近似颗粒大小。特别是，它不需要任何参数调整或手动干预。我们的方法是基于观察污染区域的统计分布是不同的，从其余的显微照片。这种非限制性的假设允许自动检测各种类型的污染物，从支撑网格的碳边缘到不同尺寸的高对比度斑点。我们证明了我们的算法使用各种实验数据集包含各种类型的污染的效率。ASOCEM作为KLT拣选机\cite{ELDAR2020107473}的一部分集成在\url{https://github.com/ShkolniskyLab/kltpicker2}上。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
