Automatic particle selection from electron micrographs using machine learning techniques

J Struct Biol. 2009 Sep;167(3):252-60. doi: 10.1016/j.jsb.2009.06.011. Epub 2009 Jun 23.


The 3D reconstruction of biological specimens using Electron Microscopy is currently capable of achieving subnanometer resolution. Unfortunately, this goal requires gathering tens of thousands of projection images that are frequently selected manually from micrographs. In this paper we introduce a new automatic particle selection that learns from the user which particles are of interest. The training phase is semi-supervised so that the user can correct the algorithm during picking and specifically identify incorrectly picked particles. By treating such errors specially, the algorithm attempts to minimize the number of false positives. We show that our algorithm is able to produce datasets with fewer wrongly selected particles than previously reported methods. Another advantage is that we avoid the need for an initial reference volume from which to generate picking projections by instead learning which particles to pick from the user. This package has been made publicly available in the open-source package Xmipp.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adenoviridae / chemistry
  • Algorithms*
  • Antigens, Viral, Tumor / chemistry
  • Artificial Intelligence*
  • Imaging, Three-Dimensional / methods*
  • Microscopy, Electron
  • Particle Size
  • Replication Protein A / chemistry


  • Antigens, Viral, Tumor
  • Replication Protein A