# ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM

arXiv: https://arxiv.org/abs/2201.06978 · HTML: https://mlsanity.com/paper/2201.06978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM](https://mlsanity.com/paper/1605.01838/) (2016)
- [A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy](https://mlsanity.com/paper/1605.05543/) (2016)
- [APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework](https://mlsanity.com/paper/1802.00469/) (2018)
- [KLT Picker: Particle Picking Using Data-Driven Optimal Templates](https://mlsanity.com/paper/1912.06500/) (2019)

## Built on by (most cited first, 1 of 1)

- [Reconstruction-Aware Cryo-EM Particle Picking](https://mlsanity.com/paper/2608.28838/) (2026)

## Abstract (arXiv)

Particle picking is currently a critical step in the cryo-electron microscopy single particle reconstruction pipeline. Contaminations in the acquired micrographs severely degrade the performance of particle pickers, resulting is many ``non-particles'' in the collected stack of particles. In this paper, we present ASOCEM (Automatic Segmentation Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which requires as an input only the approximated particle size. In particular, it does not require any parameter tuning nor manual intervention. Our method is based on the observation that the statistical distribution of contaminated regions is different from that of the rest of the micrograph. This nonrestrictive assumption allows to automatically detect various types of contaminations, from the carbon edges of the supporting grid to high contrast blobs of different sizes. We demonstrate the efficiency of our algorithm using various experimental data sets containing various types of contaminations. ASOCEM is integrated as part of the KLT picker citeELDAR2020107473 and is available at urlhttps://github.com/ShkolniskyLab/kltpicker2.