  Technological advancements in modern scientific instruments, such as scanning
electron microscopes (SEMs), have significantly increased data acquisition
rates and image resolutions enabling new questions to be explored; however, the
resulting data volumes and velocities, combined with automated experiments, are
quickly overwhelming scientists as there remain crucial steps that require
human intervention, for example reviewing image focus. We present a fast
out-of-focus detection algorithm for electron microscopy images collected
serially and demonstrate that it can be used to provide near-real-time quality
control for neuroscience workflows. Our technique, \textit{Multi-scale
Histologic Feature Detection}, adapts classical computer vision techniques and
is based on detecting various fine-grained histologic features. We exploit the
inherent parallelism in the technique to employ GPU primitives in order to
accelerate characterization. We show that our method can detect of out-of-focus
conditions within just 20ms. To make these capabilities generally available, we
deploy our feature detector as an on-demand service and show that it can be
used to determine the degree of focus in approximately 230ms, enabling
near-real-time use.
