  Trained neural networks are promising tools to analyze the ever-increasing
amount of scientific image data, but it is unclear how to best customize these
networks for the unique features in transmission electron micrographs. Here, we
systematically examine how neural network architecture choices affect how
neural networks segment, or pixel-wise separate, crystalline nanoparticles from
amorphous background in transmission electron microscopy (TEM) images. We focus
on decoupling the influence of receptive field, or the area of the input image
that contributes to the output decision, from network complexity, which
dictates the number of trainable parameters. We find that for low-resolution
TEM images which rely on amplitude contrast to distinguish nanoparticles from
background, the receptive field does not significantly influence segmentation
performance. On the other hand, for high-resolution TEM images which rely on a
combination of amplitude and phase contrast changes to identify nanoparticles,
receptive field is a key parameter for increased performance, especially in
images with minimal amplitude contrast. Our results provide insight and
guidance as to how to adapt neural networks for applications with TEM datasets.
