Abstract
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.
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Acknowledgments
Ching-Wei Wang, Hilmil Pradana and Yu-Ching Lee were supported by the Ministry of Science and Technology of Taiwan, under a grant (MOST-105-2221-E-011-121-MY2). Zhi Zhou and Hanchuan Peng were supported by Allen Institute for Brain Science, Seattle, WA, USA.
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Wang, CW., Lee, YC., Pradana, H. et al. Ensemble Neuron Tracer for 3D Neuron Reconstruction. Neuroinform 15, 185–198 (2017). https://doi.org/10.1007/s12021-017-9325-1
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DOI: https://doi.org/10.1007/s12021-017-9325-1