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Extracting a connectome from an electron microscopy (EM) data set requiresidentification of neurons and determination of synapses between neurons. Asmanual extraction of this information is very time-consuming, there has beenextensive research effort to automatically segment the neurons to help guideand eventually replace manual tracing. Until recently, there has beencomparatively less research on automatically detecting the actual synapsesbetween neurons. This discrepancy can, in part, be attributed to severalfactors: obtaining neuronal shapes is a prerequisite first step in extracting aconnectome, manual tracing is much more time-consuming than annotatingsynapses, and neuronal contact area can be used as a proxy for synapses indetermining connections.  However, recent research has demonstrated that contact area alone is not asufficient predictor of synaptic connection. Moreover, as segmentation hasimproved, we have observed that synapse annotation is consuming a moresignificant fraction of overall reconstruction time. This ratio will only getworse as segmentation improves, gating overall possible speed-up. Therefore, weaddress this problem by developing algorithms that automatically detectpre-synaptic neurons and their post-synaptic partners. In particular,pre-synaptic structures are detected using a Deep and Wide Multiscale RecursiveNetwork, and post-synaptic partners are detected using a MLP with featuresconditioned on the local segmentation.  This work is novel because it requires minimal amount of training, leveragesadvances in image segmentation directly, and provides a complete solution forpolyadic synapse detection. We further introduce novel metrics to evaluate ouralgorithm on connectomes of meaningful size. These metrics demonstrate thatcomplete automatic prediction can be used to effectively characterize mostconnectivity correctly.

Fully-Automatic Synapse Prediction and Validation on a Large Data Set