Automatic Tracing of Ultra-Volumes of Neuronal Images
H. Peng, Z. Zhou, E. Meijering, T. Zhao, G. A. Ascoli, M. Hawrylycz
Nature Methods, vol. 14, no. 4, April 2017, pp. 332-333
Despite substantial advancement in the automatic tracing of neuronal morphology in recent years, it is challenging to apply the existing algorithms to large image data sets containing billions or even trillions of voxels. Most neuron-tracing methods published to date were not designed to handle such data. We introduce UltraTracer, a solution designed to extend any base neuron-tracing algorithm to allow the tracing of ever-growing data volumes. We applied this approach to neuron-tracing algorithms with different design principles and tested it on human and mouse neuron data sets that have hundreds of billions of voxels. Results indicate that UltraTracer is scalable, accurate, and more efficient than other state-of-the-art approaches.
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