Article Information
History
- July 6, 2018.
Article Versions
- older version (March 8, 2018 - 12:51).
- You are viewing the most recent version of this article.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Author Information
- Ali Shahbazi1,+,
- Jeffery Kinnison1,2,+,
- Rafael Vescovi2,3,
- Ming Du4,
- Robert Hill5,
- Maximilian Joesch6,
- Marc Takeno7,
- Hongkui Zeng7,
- Nuno Macarico da Costa7,
- Jaime Grutzendler5,
- Narayanan Kasthuri2,3 and
- Walter J. Scheirer1,*
- 1Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
- 2Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
- 3Department of Neurobiology, University of Chicago, Chicago, IL, USA
- 4Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
- 5Department of Neurology, Yale University, New Haven, CT, USA
- 6Neuroethology Group, IST Austria, Klosterneuburg, Austria
- 7Allen Institute for Brain Science, Seattle, WA, USA
↵+ These authors contributed equally to this work.
Posted July 06, 2018.
Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing
