  Connectomics is an emerging field in neuroscience that aims to reconstruct
the 3-dimensional morphology of neurons from electron microscopy (EM) images.
Recent studies have successfully demonstrated the use of convolutional neural
networks (ConvNets) for segmenting cell membranes to individuate neurons.
However, there has been comparatively little success in high-throughput
identification of the intercellular synaptic connections required for deriving
connectivity graphs.
  In this study, we take a compositional approach to segmenting synapses,
modeling them explicitly as an intercellular cleft co-located with an
asymmetric vesicle density along a cell membrane. Instead of requiring a deep
network to learn all natural combinations of this compositionality, we train
lighter networks to model the simpler marginal distributions of membranes,
clefts and vesicles from just 100 electron microscopy samples. These feature
maps are then combined with simple rules-based heuristics derived from prior
biological knowledge.
  Our approach to synapse detection is both more accurate than previous
state-of-the-art (7% higher recall and 5% higher F1-score) and yields a 20-fold
speed-up compared to the previous fastest implementations. We demonstrate by
reconstructing the first complete, directed connectome from the largest
available anisotropic microscopy dataset (245 GB) of mouse somatosensory cortex
(S1) in just 9.7 hours on a single shared-memory CPU system. We believe that
this work marks an important step toward the goal of a microscope-pace
streaming connectomics pipeline.
