, Volume 15, Issue 2, pp 185–198

Ensemble Neuron Tracer for 3D Neuron Reconstruction

  • Ching-Wei Wang
  • Yu-Ching Lee
  • Hilmil Pradana
  • Zhi Zhou
  • Hanchuan Peng
Original Article

DOI: 10.1007/s12021-017-9325-1

Cite this article as:
Wang, CW., Lee, YC., Pradana, H. et al. Neuroinform (2017) 15: 185. doi:10.1007/s12021-017-9325-1


Tracing of neuron paths is important in neuroscience. Recent studies have shown that it is possible to segment and reconstruct three-dimensional morphology of axons and dendrites using fully automatic neuron tracing methods. A specific tracer may be better than others for a specific dataset, but another tracer could perform better for some other datasets. Ensemble of learners is an effective way to improve learning accuracy in machine learning. We developed automatic ensemble neuron tracers, which consistently perform well on 57 datasets of 5 species collected from 7 laboratories worldwide. Quantitative evaluation based on the data generated by human annotators shows that the proposed ensemble tracers are valuable for 3D neuron tracing and can be widely applied to different datasets.


3D neuron reconstruction Ensemble neuron tracer 

Supplementary material

12021_2017_9325_MOESM1_ESM.pdf (1005 kb)
(PDF 0.98 MB)
12021_2017_9325_MOESM2_ESM.7z (974 kb)
(7Z 974 KB)
12021_2017_9325_MOESM3_ESM.pdf (782 kb)
(PDF 781 KB) (985 kb)
(ZIP 985 KB)

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Graduate Institute of Biomedical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.NTUST Center of Computer Vision and Medical ImagingNational Taiwan University of Science and TechnologyTaipeiTaiwan
  3. 3.Graduate Institute of Applied Science and TechnologyNational Taiwan University of Science and TechnologyTaipeiTaiwan
  4. 4.Allen Institute for Brain ScienceSeattleUSA

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