  The promise of extracting connectomes and performing useful analysis on large
electron microscopy (EM) datasets has been an elusive dream for many years.
Tracing in even the smallest portions of neuropil requires copious human
annotation, the rate-limiting step for generating a connectome. While a
combination of improved imaging and automatic segmentation will lead to the
analysis of increasingly large volumes, machines still fail to reach the
quality of human tracers. Unfortunately, small errors in image segmentation can
lead to catastrophic distortions of the connectome.
  In this paper, to analyze very large datasets, we explore different
mechanisms that are less sensitive to errors in automation. Namely, we advocate
and deploy extensive synapse detection on the entire antennal lobe (AL)
neuropil in the brain of the fruit fly Drosophila, a region much larger than
any densely annotated to date. The resulting synapse point cloud produced is
invaluable for determining compartment boundaries in the AL and choosing
specific regions for subsequent analysis. We introduce our methodology in this
paper for region selection and show both manual and automatic synapse
annotation results. Finally, we note the correspondence between image datasets
obtained using the synaptic marker, antibody nc82, and our datasets enabling
registration between light and EM image modalities.
