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Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment

By Peter H Li, Larry F. Lindsey, Michal Januszewski, Zhihao Zheng, Alexander Shakeel Bates, István Taisz, Mike Tyka, Matthew Nichols, Feng Li, Eric Perlman, Jeremy Maitin-Shepard, Tim Blakely, Laramie Leavitt, Gregory S.X.E. Jefferis, Davi Bock, Viren Jain

Posted 11 Apr 2019
bioRxiv DOI: 10.1101/605634

Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections and dynamically adjust image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.

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