title: APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree.
abstract: BACKGROUND
  Tracing of neuron morphology is an essential technique in computational neuroscience. However, despite a number of existing methods, few open-source techniques are completely or sufficiently automated and at the same time are able to generate robust results for real 3D microscopy images.
  
  
  RESULTS
  We developed all-path-pruning 2.0 (APP2) for 3D neuron tracing. The most important idea is to prune an initial reconstruction tree of a neuron's morphology using a long-segment-first hierarchical procedure instead of the original termini-first-search process in APP. To further enhance the robustness of APP2, we compute the distance transform of all image voxels directly for a gray-scale image, without the need to binarize the image before invoking the conventional distance transform. We also design a fast-marching algorithm-based method to compute the initial reconstruction trees without pre-computing a large graph. This method allows us to trace large images. We bench-tested APP2 on ~700 3D microscopic images and found that APP2 can generate more satisfactory results in most cases than several previous methods.
  
  
  BACKGROUND
  The software has been implemented as an open-source Vaa3D plugin. The source code is available in the Vaa3D code repository http://vaa3d.org.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
authors.forename: Hang
authors.initials: H
authors.lastname: Xiao
authors.forename: Hanchuan
authors.initials: H
authors.lastname: Peng
mesh_terms: D000465:Algorithms; D000818:Animals; D004330:Drosophila / Q000166:cytology; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008853:Microscopy / Q000379:methods; D009474:Neurons / Q000166:cytology*; D012984:Software