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| 001 | 596161 | ||
| 005 | 20250715173001.0 | ||
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| 100 | 1 | _ | |a Genthe, Erik |0 P:(DE-H253)PIP1094649 |b 0 |u desy |
| 245 | _ | _ | |a PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms |
| 260 | _ | _ | |a San Diego, Calif. |c 2023 |b Elsevier |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Miletic, Sean |b 1 |
| 700 | 1 | _ | |a Tekkali, Indira |0 P:(DE-H253)PIP1099839 |b 2 |u desy |
| 700 | 1 | _ | |a Hennell James, Rory |0 P:(DE-H253)PIP1099073 |b 3 |
| 700 | 1 | _ | |a Marlovits, Thomas |0 P:(DE-H253)PIP1021412 |b 4 |e Corresponding author |
| 700 | 1 | _ | |a Heuser, Philipp |0 P:(DE-H253)PIP1023889 |b 5 |e Corresponding author |u desy |
| 773 | _ | _ | |a 10.1016/j.jsb.2023.107990 |g Vol. 215, no. 3, p. 107990 - |0 PERI:(DE-600)1469822-5 |n 3 |p 107990 |t Journal of structural biology |v 215 |y 2023 |x 1047-8477 |
| 856 | 4 | _ | |y Published on 2023-09-03. Available in OpenAccess from 2024-09-03. |z StatID:(DE-HGF)0510 |u https://bib-pubdb1.desy.de/record/596161/files/JSB-22-180_R2-3-revision.pdf |
| 856 | 4 | _ | |u https://bib-pubdb1.desy.de/record/596161/files/Pickyolo-1-s2.0-S1047847723000539-main.pdf |
| 856 | 4 | _ | |y Published on 2023-09-03. Available in OpenAccess from 2024-09-03. |x pdfa |z StatID:(DE-HGF)0510 |u https://bib-pubdb1.desy.de/record/596161/files/JSB-22-180_R2-3-revision.pdf?subformat=pdfa |
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