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Reconstructing a synaptic wiring diagram, or connectome, from electronmicroscopy (EM) images of brain tissue currently requires many hours of manualannotation or proofreading (Kasthuri and Lichtman, 2010; Lichtman and Sanes,2008; Seung, 2009). The desire to reconstruct ever larger and more complexnetworks has pushed the collection of ever larger EM datasets. A cubicmillimeter of raw imaging data would take up 1 PB of storage and present anannotation project that would be impractical without relying heavily onautomatic segmentation methods. The RhoanaNet image processing pipeline wasdeveloped to automatically segment large volumes of EM data and ease the burdenof manual proofreading and annotation. Based on (Kaynig et al., 2015), weupdated every stage of the software pipeline to provide better throughputperformance and higher quality segmentation results. We used state of the artdeep learning techniques to generate improved membrane probability maps, andGala (Nunez-Iglesias et al., 2014) was used to agglomerate 2D segments into 3Dobjects.  We applied the RhoanaNet pipeline to four densely annotated EM datasets, twofrom mouse cortex, one from cerebellum and one from mouse lateral geniculatenucleus (LGN). All training and test data is made available for benchmarkcomparisons. The best segmentation results obtained gave$V^\text{Info}_\text{F-score}$ scores of 0.9054 and 09182 for the cortexdatasets, 0.9438 for LGN, and 0.9150 for Cerebellum.  The RhoanaNet pipeline is open source software. All source code, trainingdata, test data, and annotations for all four benchmark datasets are availableat www.rhoana.org.

RhoanaNet Pipeline: Dense Automatic Neural Annotation