Cryo-EM Single Particle Analysis workflows require tens of thousands of high-quality particle projections to unveil the three-dimensional structure of macromolecules. Conventional methods for automatic particle picking tend to suffer from high false-positive rates, hampering the reconstruction process. One common cause of this problem is the presence of carbon and different types of high-contrast contaminations. In order to overcome this limitation, we have developed MicrographCleaner, a deep learning package designed to discriminate, in an automated fashion, between regions of micrographs which are suitable for particle picking, and those which are not. MicrographCleaner implements a U-net-like deep learning model trained on a manually curated dataset compiled from over five hundred micrographs. The benchmarking, carried out on approximately one hundred independent micrographs, shows that MicrographCleaner is a very efficient approach for micrograph preprocessing. MicrographCleaner (micrograph_cleaner_em) package is available at PyPI and Anaconda Cloud and also as a Scipion/Xmipp protocol. Source code is available at https://github.com/rsanchezgarc/micrograph_cleaner_em.

中文翻译：

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MicrographCleaner：一个使用深度学习进行冷冻-EM显微照片清洗的python软件包。

Cryo-EM单粒子分析工作流程需要数以万计的高质量粒子投影才能揭示大分子的三维结构。用于自动粒子拾取的常规方法趋于遭受高假阳性率，从而妨碍了重建过程。造成此问题的一个常见原因是存在碳和不同类型的高对比度污染物。为了克服这一限制，我们开发了MicrographCleaner，这是一种深度学习软件包，旨在自动识别适合颗粒采集的显微照片区域和不适合颗粒采集的显微照片区域。MicrographCleaner实现了一个类似U-net的深度学习模型，该模型在由500多个显微照片汇编而成的手动精选数据集上进行了训练。基准测试 对大约一百张独立显微照片进行的测试表明，MicrographCleaner是显微照片预处理的一种非常有效的方法。MicrographCleaner（micrograph_cleaner_em）软件包可从PyPI和Anaconda Cloud获得，也可作为Scipion / Xmipp协议获得。源代码位于https://github.com/rsanchezgarc/micrograph_cleaner_em。