%0 Journal Article
%A Genthe, Erik
%A Miletic, Sean
%A Tekkali, Indira
%A Hennell James, Rory
%A Marlovits, Thomas
%A Heuser, Philipp
%T PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms
%J Journal of structural biology
%V 215
%N 3
%@ 1047-8477
%C San Diego, Calif.
%I Elsevier
%M PUBDB-2023-06092
%P 107990
%D 2023
%Z ich habe keinen Zugriff auf den Volltext, Daniela Waiting for fulltext
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 37364763
%U <Go to ISI:>//WOS:001036383000001
%R 10.1016/j.jsb.2023.107990
%U https://bib-pubdb1.desy.de/record/596161