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The promise of extracting connectomes and performing useful analysis on largeelectron microscopy (EM) datasets has been an elusive dream for many years.Tracing in even the smallest portions of neuropil requires copious humanannotation, the rate-limiting step for generating a connectome. While acombination of improved imaging and automatic segmentation will lead to theanalysis of increasingly large volumes, machines still fail to reach thequality of human tracers. Unfortunately, small errors in image segmentation canlead to catastrophic distortions of the connectome.  In this paper, to analyze very large datasets, we explore differentmechanisms that are less sensitive to errors in automation. Namely, we advocateand deploy extensive synapse detection on the entire antennal lobe (AL)neuropil in the brain of the fruit fly Drosophila, a region much larger thanany densely annotated to date. The resulting synapse point cloud produced isinvaluable for determining compartment boundaries in the AL and choosingspecific regions for subsequent analysis. We introduce our methodology in thispaper for region selection and show both manual and automatic synapseannotation results. Finally, we note the correspondence between image datasetsobtained using the synaptic marker, antibody nc82, and our datasets enablingregistration between light and EM image modalities.

Large-scale EM Analysis of the Drosophila Antennal Lobe with Automatically Computed Synapse Point Clouds