Automated reconstruction of a serial-section EM drosophila brain with flood-filling networks and local realignment.

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bioRxiv. 2019 Apr 11;. doi: 10.1101/605634

Li Peter, Lindsey Larry, Januszewski Michał, Zheng Zhihao, Bates Alexander, Taisz István, Tyka Mike, Nichols Matthew, Li Feng, Perlman Eric, Maitin-Shepard Jeremy, Blakely Tim, Leavitt Laramie, Jefferis Gregory, Bock Davi, Jain Viren

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Abstract

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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Automated reconstruction of a serial-section EM drosophila brain with flood-filling networks and local realignment.

Abstract

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