title: An automatic particle pickup method using a neural network applicable to low-contrast electron micrographs.
abstract: Three-dimensional reconstruction from electron micrographs requires the selection of many single-particle projection images; more than 10 000 are generally required to obtain 5- to 10-A structural resolution. Consequently, various automatic detection algorithms have been developed and successfully applied to large symmetric protein complexes. This paper presents a new automated particle recognition and pickup procedure based on the three-layer neural network that has a large application range than other automated procedures. Its use for both faint and noisy electron micrographs is demonstrated. The method requires only 200 selected particles as learning data and is able to detect images of proteins as small as 200 kDa.
authors.affiliation: Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8568, Japan.
authors.forename: T
authors.initials: T
authors.lastname: Ogura
authors.forename: C
authors.initials: C
authors.lastname: Sato
mesh_terms: D000818:Animals; D001331:Automation; D004557:Electric Organ / Q000648:ultrastructure; D004593:Electrophorus; D007091:Image Processing, Computer-Assisted; D008854:Microscopy, Electron / Q000379:methods*; D009415:Nerve Net; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015222:Sodium Channels / Q000648:ultrastructure*
chemical_list: D015222:Sodium Channels