TY - JOUR
T1 - ASOCEM
T2 - Automatic Segmentation Of Contaminations in cryo-EM
AU - Eldar, Amitay
AU - Amos, Ido
AU - Shkolnisky, Yoel
N1 - Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
KW - Chan-Vese model active contours
KW - Contamination detection and segmentation
KW - Cryo-electron microscopy
KW - Single-particle reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85131538130&partnerID=8YFLogxK
U2 - 10.1016/j.jsb.2022.107871
DO - 10.1016/j.jsb.2022.107871
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C2 - 35609785
AN - SCOPUS:85131538130
SN - 1047-8477
VL - 214
JO - Journal of Structural Biology
JF - Journal of Structural Biology
IS - 3
M1 - 107871
ER -