  The emerging field of connectomics aims to unlock the mysteries of the brain
by understanding the connectivity between neurons. To map this connectivity, we
acquire thousands of electron microscopy (EM) images with nanometer-scale
resolution. After aligning these images, the resulting dataset has the
potential to reveal the shapes of neurons and the synaptic connections between
them. However, imaging the brain of even a tiny organism like the fruit fly
yields terabytes of data. It can take years of manual effort to examine such
image volumes and trace their neuronal connections. One solution is to apply
image segmentation algorithms to help automate the tracing tasks. In this
paper, we propose a novel strategy to apply such segmentation on very large
datasets that exceed the capacity of a single machine. Our solution is robust
to potential segmentation errors which could otherwise severely compromise the
quality of the overall segmentation, for example those due to poor classifier
generalizability or anomalies in the image dataset. We implement our algorithms
in a Spark application which minimizes disk I/O, and apply them to a few large
EM datasets, revealing both their effectiveness and scalability. We hope this
work will encourage external contributions to EM segmentation by providing 1) a
flexible plugin architecture that deploys easily on different cluster
environments and 2) an in-memory representation of segmentation that could be
conducive to new advances.
