ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM

Amitay Eldar, Ido Amos, Yoel Shkolnisky

Research output: Contribution to journalArticlepeer-review

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 in 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 approximate 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 (Eldar et al., 2020) and is available at https://github.com/ShkolniskyLab/kltpicker2.

Original languageEnglish
Article number107871
JournalJournal of Structural Biology
Volume214
Issue number3
DOIs
StatePublished - Sep 2022

Keywords

  • Chan-Vese model active contours
  • Contamination detection and segmentation
  • Cryo-electron microscopy
  • Single-particle reconstruction

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