title: Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images.
abstract: BACKGROUND
  In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming.
  
  
  METHODS
  We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links.
  
  
  RESULTS
  We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results.
  
  
  METHODS
  Post-automatic correction methods have also been used in Mishchenko et al. (2010) and Haehn et al. (2014). These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as Jeong et al. (2009) and Cardona et al. (2010) and are inherently different than our method.
  
  
  CONCLUSIONS
  Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication.
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah, United States; Department of Electrical and Computer Engineering, University of Utah, United States.
authors.forename: Cory
authors.initials: C
authors.lastname: Jones
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah, United States; School of Computing, University of Utah, United States.
authors.forename: Ting
authors.initials: T
authors.lastname: Liu
authors.affiliation: National Center for Microscopy and Imaging Research, University of California, San Diego, United States.
authors.forename: Nathaniel Wood
authors.initials: NW
authors.lastname: Cohan
authors.affiliation: National Center for Microscopy and Imaging Research, University of California, San Diego, United States.
authors.forename: Mark
authors.initials: M
authors.lastname: Ellisman
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah, United States; Department of Electrical and Computer Engineering, University of Utah, United States; School of Computing, University of Utah, United States. Electronic address: tolga@sci.utah.edu.
authors.forename: Tolga
authors.initials: T
authors.lastname: Tasdizen
mesh_terms: D000818:Animals; D063132:Connectome; D021621:Imaging, Three-Dimensional; D008855:Microscopy, Electron, Scanning; D008959:Models, Neurological; D009474:Neurons / Q000378:metabolism / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated
keywords: 3D segmentation; Connectomics; Electron microscopy; Image segmentation; Neuron reconstruction; Semi-automatic segmentation