# APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework

arXiv: https://arxiv.org/abs/1802.00469 · HTML: https://mlsanity.com/paper/1802.00469/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis](https://mlsanity.com/paper/2311.05046/) (2023)
- [Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs](https://mlsanity.com/paper/2311.16140/) (2023)
- [Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM](https://mlsanity.com/paper/2102.06125/) (2021)
- [Multi-target detection with rotations](https://mlsanity.com/paper/2101.07709/) (2021)
- [ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM](https://mlsanity.com/paper/2201.06978/) (2022)
- [Advanced POD-Based Performance Evaluation of Classifiers Applied to Human Driver Lane Changing Prediction](https://mlsanity.com/paper/2408.15722/) (2024)
- [Reconstruction-Aware Cryo-EM Particle Picking](https://mlsanity.com/paper/2608.28838/) (2026)

## Abstract (arXiv)

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.