In order to understand how changes to a material at the atomic and nano-scales impact the way a material behaves, it is necessary to measure the material at that scale. Scanning transmission Electron Microscopy (STEM) is one such approach. Over the last 5 years, computer vision has been completely revolutionized by the advent of deep neural networks (DNN). One challenge of using DNNs for image processing is that they require often large amounts of labelled data in order to build a usable algorithm (model). Researchers at Pacific Northwest National Laboratory (PNNL) have developed a few-shot algorithm which allows the model to be operated using limited data. Using data from a sample micrograph, a new neural network is implemented with the objective of aptly segmenting the micrograph and obtaining classifications for the microstructures. This model investigates the use of existing image segmentation techniques, particularly region-based techniques. The success of this approach can provide a rapid and reconfigurable tool for identifying these microstructures. This research conducted in collaboration with Pacific Northwest National Laboratory.