Particle picking is currently a critical step in the cryo-EM single particle reconstruction pipeline. Despite extensive work on this problem, for many data sets it is still challenging, especially for low SNR micrographs. We present the KLT (Karhunen Loeve Transform) picker, which is fully automatic and requires as an input only the approximated particle size. In particular, it does not require any manual picking. Our method is designed especially to handle low SNR micrographs. It is based on learning a set of optimal templates through the use of multi-variate statistical analysis via the Karhunen Loeve Transform. We evaluate the KLT picker on publicly available data sets and present high-quality results with minimal manual effort.

中文翻译：

---

KLT选择器：使用数据驱动的最佳模板进行粒子选择。

当前，粒子拾取是cryo-EM单粒子重建管道中的关键步骤。尽管在此问题上进行了大量工作，但对于许多数据集而言，仍然具有挑战性，尤其是对于低SNR显微照片。我们介绍了KLT（Karhunen Loeve变换）选择器，该选择器是全自动的，仅需要输入近似的粒度即可。特别是，它不需要任何手动拣配。我们的方法专为处理低SNR显微照片而设计。它基于通过Karhunen Loeve变换进行的多元统计分析，学习了一组最佳模板。我们根据可公开获得的数据集评估KLT选择器，并以最少的人工工作即可提供高质量的结果。