KLT Picker: Particle Picking Using Data-Driven Optimal Templates
This addresses a critical bottleneck in cryo-EM for researchers, offering an incremental improvement by automating template learning for low SNR conditions.
The paper tackles the problem of particle picking in cryo-EM reconstruction, especially for low SNR micrographs, by introducing the KLT picker, a fully automatic method that learns optimal templates using the Karhunen Loeve Transform, achieving high-quality results with minimal manual effort.
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.