  Heterogeneous catalysts possess complex surface and bulk structures,
relatively poor intrinsic contrast, and often a sparse distribution of the
catalytic nanoparticles (NPs), posing a significant challenge for image
segmentation, including the current state-of-the-art deep learning methods. To
tackle this problem, we apply a deep learning-based approach for the
multi-class semantic segmentation of a $\gamma$-Alumina/Pt catalytic material
in a class imbalance situation. Specifically, we used the weighted focal loss
as a loss function and attached it to the U-Net's fully convolutional network
architecture. We assessed the accuracy of our results using Dice similarity
coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on
the overlap between the ground-truth and predicted segmentations. Our adopted
U-Net model with the weighted focal loss function achieved an average DSC score
of 0.96 $\pm$ 0.003 in the $\gamma$-Alumina support material and 0.84 $\pm$
0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap
error of less than 2 nm at the 90th percentile of HD for $\gamma$-Alumina and
Pt NPs segmentations. The complex surface morphology of the $\gamma$-Alumina
and its relation to the Pt NPs were visualized in 3D by the deep
learning-assisted automatic segmentation of a large data set of high-angle
annular dark-field (HAADF) scanning transmission electron microscopy (STEM)
tomography reconstructions.
