  Synaptic connectivity detection is a critical task for neural reconstruction
from Electron Microscopy (EM) data. Most of the existing algorithms for synapse
detection do not identify the cleft location and direction of connectivity
simultaneously. The few methods that computes direction along with contact
location have only been demonstrated to work on either dyadic (most common in
vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on
both types. In this paper, we present an algorithm to automatically predict the
location as well as the direction of both dyadic and polyadic synapses. The
proposed algorithm first generates candidate synaptic connections from
voxelwise predictions of signed proximity generated by a 3D U-net. A second 3D
CNN then prunes the set of candidates to produce the final detection of cleft
and connectivity orientation. Experimental results demonstrate that the
proposed method outperforms the existing methods for determining synapses in
both rodent and fruit fly brain.
