https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2022Eldar_ASOCEM2022Eldar ASOCEM - Revision history2026-06-13T12:13:22ZRevision history for this page on the wikiMediaWiki 1.44.2https://3demmethods.i2pc.es/index.php?title=2022Eldar_ASOCEM&diff=4327&oldid=prevWikiSysop: Created page with "== Citation == Eldar, Amitay / Amos, Ido / Shkolnisky, Yoel ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM 2022. Journal of Structural Biology, Vol. 214, No. 3..."2023-06-14T07:58:27Z<p>Created page with "== Citation == Eldar, Amitay / Amos, Ido / Shkolnisky, Yoel ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM 2022. Journal of Structural Biology, Vol. 214, No. 3..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
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Eldar, Amitay / Amos, Ido / Shkolnisky, Yoel <br />
ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM <br />
2022. Journal of Structural Biology, Vol. 214, No. 3, p. 107871 <br />
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== Abstract ==<br />
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Particle picking is currently a critical step in the cryo-electron microscopy single particle reconstruction pipeline.<br />
Contaminations in the acquired micrographs severely degrade the performance of particle pickers, resulting in<br />
many “non-particles” in the collected stack of particles. In this paper, we present ASOCEM (Automatic Segmentation<br />
Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which<br />
requires as an input only the approximate particle size. In particular, it does not require any parameter tuning<br />
nor manual intervention. Our method is based on the observation that the statistical distribution of contaminated<br />
regions is different from that of the rest of the micrograph. This nonrestrictive assumption allows to automatically<br />
detect various types of contaminations, from the carbon edges of the supporting grid to high contrast blobs<br />
of different sizes. We demonstrate the efficiency of our algorithm using various experimental data sets containing<br />
various types of contaminations. ASOCEM is integrated as part of the KLT picker (Eldar et al., 2020) and is<br />
available at https://github.com/ShkolniskyLab/kltpicker2.<br />
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== Keywords ==<br />
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== Links ==<br />
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https://www.sciencedirect.com/science/article/pii/S1047847720300356<br />
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== Related software ==<br />
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== Related methods ==<br />
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== Comments ==</div>WikiSysop