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

DOI PubMed

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.

Copyright © 2017 by the authors. Published version © 2017 by Springer Nature. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

Copyright © 1996 - 2017 Erik Meijering