2023Genthe PickYolo
Citation
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
Abstract
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.
Keywords
Links
https://www.sciencedirect.com/science/article/pii/S1047847723000539
