ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM

TL;DR

ASOCEM is an automatic, parameter-free method for detecting and segmenting contaminations in cryo-EM micrographs, improving particle picking accuracy in cryo-EM reconstruction pipelines.

Contribution

It introduces a novel automatic segmentation technique based on statistical distribution differences, requiring only approximate particle size and no manual tuning.

Findings

Effectively detects various contamination types in cryo-EM images.

No manual parameter tuning needed for different contamination types.

Integrated into existing particle picker for improved performance.

Abstract

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 \cite{ELDAR2020107473} and is available at \url{https://github.com/ShkolniskyLab/kltpicker2}.