  The tracing of neural pathways through large volumes of image data is an
incredibly tedious and time-consuming process that significantly encumbers
progress in neuroscience. We are exploring deep learning's potential to
automate segmentation of high-resolution scanning electron microscope (SEM)
image data to remove that barrier. We have started with neural pathway tracing
through 5.1GB of whole-brain serial-section slices from larval zebrafish
collected by the Center for Brain Science at Harvard University. This kind of
manual image segmentation requires years of careful work to properly trace the
neural pathways in an organism as small as a zebrafish larva (approximately 5mm
in total body length). In automating this process, we would vastly improve
productivity, leading to faster data analysis and breakthroughs in
understanding the complexity of the brain. We will build upon prior attempts to
employ deep learning for automatic image segmentation extending methods for
unconventional deep learning data.
