The field of connectomics faces unprecedented "big data" challenges. To
reconstruct neuronal connectivity, automated pixel-level segmentation is
required for petabytes of streaming electron microscopy data. Existing
algorithms provide relatively good accuracy but are unacceptably slow, and
would require years to extract connectivity graphs from even a single cubic
millimeter of neural tissue. Here we present a viable real-time solution, a
multi-pass pipeline optimized for shared-memory multicore systems, capable of
processing data at near the terabyte-per-hour pace of multi-beam electron
microscopes. The pipeline makes an initial fast-pass over the data, and then
makes a second slow-pass to iteratively correct errors in the output of the
fast-pass. We demonstrate the accuracy of a sparse slow-pass reconstruction
algorithm and suggest new methods for detecting morphological errors. Our
fast-pass approach provided many algorithmic challenges, including the design
and implementation of novel shallow convolutional neural nets and the
parallelization of watershed and object-merging techniques. We use it to
reconstruct, from image stack to skeletons, the full dataset of Kasthuri et al.
(463 GB capturing 120,000 cubic microns) in a matter of hours on a single
multicore machine rather than the weeks it has taken in the past on much larger
distributed systems.

该研究提出了一种基于多核系统的、可用于实时处理电子显微镜数据的多通道管道，并演示了其在重建神经元连接性的准确性和速度上的优势。