  Single-beam scanning electron microscopes (SEM) are widely used to acquire
massive data sets for biomedical study, material analysis, and fabrication
inspection. Datasets are typically acquired with uniform acquisition: applying
the electron beam with the same power and duration to all image pixels, even if
there is great variety in the pixels' importance for eventual use. Many SEMs
are now able to move the beam to any pixel in the field of view without delay,
enabling them, in principle, to invest their time budget more effectively with
non-uniform imaging.
  In this paper, we show how to use deep learning to accelerate and optimize
single-beam SEM acquisition of images. Our algorithm rapidly collects an
information-lossy image (e.g. low resolution) and then applies a novel learning
method to identify a small subset of pixels to be collected at higher
resolution based on a trade-off between the saliency and spatial diversity. We
demonstrate the efficacy of this novel technique for active acquisition by
speeding up the task of collecting connectomic datasets for neurobiology by up
to an order of magnitude.
