  Finding quantitative descriptors representing the microstructural features of
a given material is an ongoing research area in the paradigm of
Materials-by-Design. Historically, microstructural analysis mostly relies on
qualitative descriptions. However, to build a robust and accurate
process-structure-properties relationship, which is required for designing new
advanced high-performance materials, the extraction of quantitative and
meaningful statistical data from the microstructural analysis is a critical
step. In recent years, computer vision (CV) methods, especially those which are
centered around convolutional neural network (CNN) algorithms have shown
promising results for this purpose. This review paper focuses on the
state-of-the-art CNN-based techniques that have been applied to various
multi-scale microstructural image analysis tasks, including classification,
object detection, segmentation, feature extraction, and reconstruction.
Additionally, we identified the main challenges with regard to the application
of these methods to materials science research. Finally, we discussed some
possible future directions of research in this area. In particular, we
emphasized the application of transformer-based models and their capabilities
to improve the microstructural analysis of materials.
