  Automatized object identification and feature analysis of experimental image
data are indispensable for data-driven material science; deep-learning-based
segmentation algorithms have been shown to be a promising technique to achieve
this goal. However, acquiring high-resolution experimental images and assigning
labels in order to train such algorithms is challenging and costly in terms of
both time and labor. In the present work, we apply synthetic images, which
resemble the experimental image data in terms of geometrical and visual
features, to train state-of-art deep learning-based Mask R-CNN algorithms to
segment vanadium pentoxide (V2O5) nanowires, a canonical cathode material,
within optical intensity-based images from spectromicroscopy. The performance
evaluation demonstrates that even though the deep learning model is trained on
pure synthetically generated structures, it can segment real optical
intensity-based spectromicroscopy images of complex V2O5 nanowire structures in
overlapped particle networks, thus providing reliable statistical information.
The model can further be used to segment nanowires in scanning electron
microscopy (SEM) images, which are fundamentally different from the training
dataset known to the model. The proposed methodology of using a purely
synthetic dataset to train the deep learning model can be extended to any
optical intensity-based images of variable particle morphology, extent of
agglomeration, material class, and beyond.
