  Atom-probe tomography (APT) facilitates nano- and atomic-scale
characterization and analysis of microstructural features. Specifically, APT is
well suited to study the interfacial properties of granular or heterophase
systems. Traditionally, the identification of the interface between, for
precipitate and matrix phases, in APT data has been obtained either by
extracting iso-concentration surfaces based on a user-supplied concentration
value or by manually perturbing the concentration value until the
iso-concentration surface qualitatively matches the interface. These approaches
are subjective, not scalable, and may lead to inconsistencies due to local
composition inhomogeneities.
  We propose a digital image segmentation approach based on deep neural
networks that transfer learned knowledge from natural images to automatically
segment the data obtained from APT into different phases. This approach not
only provides an efficient way to segment the data and extract interfacial
properties but does so without the need for expensive interface labeling for
training the segmentation model.
  We consider here a system with a precipitate phase in a matrix and with three
different interface modalities---layered, isolated, and interconnected---that
are obtained for different relative geometries of the precipitate phase. We
demonstrate the accuracy of our segmentation approach through qualitative
visualization of the interfaces, as well as through quantitative comparisons
with proximity histograms obtained by using more traditional approaches.
