APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Feb 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
7 papers.
- 2023On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysiscited by 5
- 2023Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographscited by 3
- 2021Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EMcited by 2
- 2021Multi-target detection with rotationscited by 1
- 2022ASOCEM: Automatic Segmentation Of Contaminations in cryo-EMcited by 1
- 2024Advanced POD-Based Performance Evaluation of Classifiers Applied to Human Driver Lane Changing Predictioncited by 0
- 2026Reconstruction-Aware Cryo-EM Particle Pickingcited by 0
- 2026Reconstruction-Aware Cryo-EM Particle Picking28 Aug 2026
- 2024Advanced POD-Based Performance Evaluation of Classifiers Applied to Human Driver Lane Changing Prediction28 Aug 2024
- 2023On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis8 Nov 2023
- 2023Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs4 Nov 2023
- 2022ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM18 Jan 2022
- 2021Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM11 Feb 2021
- 2021Multi-target detection with rotations19 Jan 2021