https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2023Genthe_PickYolo 2026-05-24T21:15:07Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2023Genthe_PickYolo&diff=4474&oldid=prev 2023-09-20T06:12:38Z

<p>Created page with &quot;== Citation == Genthe, Erik / Miletic, Sean / Tekkali, Indira / James, Rory Hennell / Marlovits, Thomas C. / Heuser, Philipp. PickYOLO: Fast deep learning particle detector f...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Genthe, Erik / Miletic, Sean / Tekkali, Indira / James, Rory Hennell / Marlovits, Thomas C. / Heuser, Philipp. PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms. 2023. J. Structural Biology, 215, p. 107990 <br /> <br /> == Abstract ==<br /> <br /> Particle localization (picking) in digital tomograms is a laborious and time-intensive step in cryogenic electron tomography (cryoET) analysis often requiring considerable user involvement, thus becoming a bottleneck for automated cryoET subtomogram averaging (STA) pipelines. In this paper, we introduce a deep learning framework called PickYOLO to tackle this problem. PickYOLO is a super-fast, universal particle detector based on the deep-learning real-time object recognition system YOLO (You Only Look Once), and tested on single particles, filamentous structures, and membrane-embedded particles. After training with the centre coordinates of a few hundred representative particles, the network automatically detects additional particles with high yield and reliability at a rate of 0.24–3.75 s per tomogram. PickYOLO can automatically detect number of particles comparable to those manually selected by experienced microscopists. This makes PickYOLO a valuable tool to substantially reduce the time and manual effort needed to analyse cryoET data for STA, greatly aiding in high-resolution cryoET structure determination.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S1047847723000539<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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