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Automatic Tracing of Ultra-Volume of Neuronal Images

Hanchuan Peng, Zhi Zhou, Erik Meijering, Ting Zhao, Giorgio A. Ascoli, Michael Hawrylycz
doi: https://doi.org/10.1101/087726
Hanchuan Peng
1Allen Institute for Brain Science, Seattle, WA, USA
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  • For correspondence: hanchuanp@alleninstitute.org
Zhi Zhou
1Allen Institute for Brain Science, Seattle, WA, USA
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Erik Meijering
2Erasmus University Medical Center Rotterdam, Rotterdam, Netherlands
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Ting Zhao
3Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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Giorgio A. Ascoli
4Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
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Michael Hawrylycz
1Allen Institute for Brain Science, Seattle, WA, USA
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Abstract

Despite substantial advancement in the automatic tracing of neurons' morphology in recent years, it is challenging to apply the existing algorithms to very large image datasets containing billions or more voxels. We introduce UltraTracer, a solution designed to extend any base neuron-tracing algorithm to be able to trace virtually unlimited data volumes. We applied this approach to neuron-tracing algorithms with completely different design principles and tested on challenging human and mouse neuron datasets that have hundreds of billions of voxels. Results indicate that UltraTracer is scalable, accurate, and about 3 to 6 times more efficient compared to other state-of-the-art approaches.

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Posted November 14, 2016.
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Automatic Tracing of Ultra-Volume of Neuronal Images
Hanchuan Peng, Zhi Zhou, Erik Meijering, Ting Zhao, Giorgio A. Ascoli, Michael Hawrylycz
bioRxiv 087726; doi: https://doi.org/10.1101/087726
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Automatic Tracing of Ultra-Volume of Neuronal Images
Hanchuan Peng, Zhi Zhou, Erik Meijering, Ting Zhao, Giorgio A. Ascoli, Michael Hawrylycz
bioRxiv 087726; doi: https://doi.org/10.1101/087726

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