title: DP2: Distributed 3D image segmentation using micro-labor workforce.
abstract: CONCLUSIONS
  This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon's Mechanical Turk system provides the labor in a scalable manner.
  
  
  METHODS
  Python-based code for non-commercial use and test data are available in the source archive at https://sites.google.com/site/imagecrowdseg/.
  
  
  BACKGROUND
  rgiuly@ucsd.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA. rgiuly@ucsd.edu
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Giuly
authors.forename: Keun-Young
authors.initials: KY
authors.lastname: Kim
authors.forename: Mark H
authors.initials: MH
authors.lastname: Ellisman
mesh_terms: D001369:Axons / Q000648:ultrastructure; D021621:Imaging, Three-Dimensional; D008853:Microscopy; D009186:Myelin Sheath / Q000648:ultrastructure; D011381:Programming Languages; D012984:Software