Ensemble Neuron Tracer for 3D Neuron Reconstruction

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 ano...

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Bibliographic Details
Published in:Neuroinformatics (Totowa, N.J.) N.J.), 2017-04, Vol.15 (2), p.185-198
Main Authors: Wang, Ching-Wei, Lee, Yu-Ching, Pradana, Hilmil, Zhou, Zhi, Peng, Hanchuan
Format: Article
Language:English
Subjects:
Algorithms
Animals
Automatic Data Processing
Axons
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Brain - cytology
Brain - diagnostic imaging
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Databases, Factual
Datasets
Dendrites
Dendrites - ultrastructure
Humans
Imaging, Three-Dimensional - methods
Learning algorithms
Magnetic Resonance Imaging
Nervous system
Neurology
Neurons - physiology
Neurons - ultrastructure
Neurosciences
Original Article
Quantitative analysis
Tracers
Citations: Items that this one cites
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Summary: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.
ISSN:1539-2791
1559-0089
DOI:10.1007/s12021-017-9325-1