Automating the analysis
portion of materials characterization by
electron microscopy (EM) has the potential to accelerate the process
of scientific discovery. To this end, we present a Bayesian deep-learning
model for semantic segmentation and localization of particle instances
in EM images. These segmentations can subsequently be used to compute
quantitative measures such as particle-size distributions, radial-
distribution functions, average sizes, and aspect ratios of the particles
in an image. Moreover, by making use of the epistemic uncertainty
of our model, we obtain uncertainty estimates of its outputs and use
these to filter out false-positive predictions and hence produce more
accurate quantitative measures. We incorporate our method into the
ImageDataExtractor package, as ImageDataExtractor 2.0, which affords
a full pipeline to automatically extract particle information for
large-scale data-driven materials discovery. Finally, we present and
make publicly available the Electron Microscopy Particle Segmentation
(EMPS) data set. This is the first human-labeled particle instance
segmentation data set, consisting of 465 EM images and their corresponding
semantic instance segmentation maps.