Recent advances in scientific instruments have resulted in dramatic increase in the volumes and velocities of data being generated in every-day laboratories. Scanning electron microscopy is one such example where technological advancements are now overwhelming scientists with critical data for montaging, alignment, and image segmentation -- key practices for many scientific domains, including, for example, neuroscience, where they are used to derive the anatomical relationships of the brain. These instruments now necessitate equally advanced computing resources and techniques to realize their full potential. Here 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 neurology research. Our technique, Multi-scale Histologic Feature Detection, adapts classical computer vision techniques and is based on detecting various fine-grained histologic features. We further exploit the inherent parallelism in the technique by employing GPGPU primitives in order to accelerate characterization. Tests are performed that demonstrate near-real-time detection of out-of-focus conditions. We deploy these capabilities as a funcX function and show that it can be applied as data are collected using an automated pipeline . We discuss extensions that enable scaling out to support multi-beam microscopes and integration with existing focus systems for purposes of implementing auto-focus.
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