  Phase contrast transmission electron microscopy (TEM) is a powerful tool for
imaging the local atomic structure of materials. TEM has been used heavily in
studies of defect structures of 2D materials such as monolayer graphene due to
its high dose efficiency. However, phase contrast imaging can produce complex
nonlinear contrast, even for weakly-scattering samples. It is therefore
difficult to develop fully-automated analysis routines for phase contrast TEM
studies using conventional image processing tools. For automated analysis of
large sample regions of graphene, one of the key problems is segmentation
between the structure of interest and unwanted structures such as surface
contaminant layers. In this study, we compare the performance of a conventional
Bragg filtering method to a deep learning routine based on the U-Net
architecture. We show that the deep learning method is more general, simpler to
apply in practice, and produces more accurate and robust results than the
conventional algorithm. We provide easily-adaptable source code for all results
in this paper, and discuss potential applications for deep learning in
fully-automated TEM image analysis.
