APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework
This addresses the need for efficient and unbiased particle selection in cryo-EM, which is crucial for high-resolution reconstruction but often requires manual effort, though it appears incremental as it builds on template matching inspiration.
The paper tackles the problem of time-consuming and potentially biased particle picking in cryo-EM by introducing APPLE Picker, a fully automatic and template-free method that achieves fast and accurate results on datasets like β-galactosidase and keyhole limpet hemocyanin.
Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While semi-automated particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, semi-automated particle picking is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and fully automatic particle picking. While our approach was inspired by template matching, it is completely template-free. This approach is evaluated on publicly available datasets containing micrographs of $β$-galactosidase and keyhole limpet hemocyanin projections.