title: Automated identification of microtubules in cellular electron tomography.
abstract: We describe a method for automatically finding the location and conformations of microtubules in tomograms of high-pressure frozen, freeze substituted cells. Our approach uses two steps: a preprocessing step that finds locations in the tomograms that are likely to lie inside microtubules and a tracking step that uses the preprocessed data to identify the trajectories of individual microtubules. We test this method on a reconstruction of a Caenorhabditis elegans mitotic spindle and we compare our results with those obtained by a human expert who manually segmented the same data. At present, the method could be used to assist the analysis of large-scale tomography reconstructions. With further improvements, it may be possible to reliably segment cellular tomograms without human intervention.
authors: Department of Physics, Harvard University, Cambridge, Massachusetts 01238, USA.
authors: Daniyar
authors: D
authors: Nurgaliev
authors: Timur
authors: T
authors: Gatanov
authors: Daniel J
authors: DJ
authors: Needleman
mesh_terms: D000818:Animals; D002477:Cells / Q000378:metabolism* / Q000648:ultrastructure*; D055032:Electron Microscope Tomography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008870:Microtubules / Q000378:metabolism / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D054928:Validation Studies as Topic