  An open challenge problem at the forefront of modern neuroscience is to
obtain a comprehensive mapping of the neural pathways that underlie human brain
function; an enhanced understanding of the wiring diagram of the brain promises
to lead to new breakthroughs in diagnosing and treating neurological disorders.
Inferring brain structure from image data, such as that obtained via electron
microscopy (EM), entails solving the problem of identifying biological
structures in large data volumes. Synapses, which are a key communication
structure in the brain, are particularly difficult to detect due to their small
size and limited contrast. Prior work in automated synapse detection has relied
upon time-intensive biological preparations (post-staining, isotropic slice
thicknesses) in order to simplify the problem.
  This paper presents VESICLE, the first known approach designed for mammalian
synapse detection in anisotropic, non-post-stained data. Our methods explicitly
leverage biological context, and the results exceed existing synapse detection
methods in terms of accuracy and scalability. We provide two different
approaches - one a deep learning classifier (VESICLE-CNN) and one a lightweight
Random Forest approach (VESICLE-RF) to offer alternatives in the
performance-scalability space. Addressing this synapse detection challenge
enables the analysis of high-throughput imaging data soon expected to reach
petabytes of data, and provide tools for more rapid estimation of brain-graphs.
Finally, to facilitate community efforts, we developed tools for large-scale
object detection, and demonstrated this framework to find $\approx$ 50,000
synapses in 60,000 $\mu m ^3$ (220 GB on disk) of electron microscopy data.
