  The science of fractography revolves around the correlation between
topographic characteristics of the fracture surface and the mechanisms and
external conditions leading to their creation. While being a topic of
investigation for centuries, it has remained mostly qualitative to date. A
quantitative analysis of fracture surfaces is of prime interest for both the
scientific community and the industrial sector, bearing the potential for
improved understanding on the mechanisms controlling the fracture process and
at the same time assessing the reliability of computational models currently
being used for material design. With new advances in the field of image
analysis, and specifically with machine learning tools becoming more accessible
and reliable, it is now feasible to automate the process of extracting
meaningful information from fracture surface images. Here, we propose a method
of identifying and quantifying the relative appearance of intergranular and
transgranular fracture events from scanning electron microscope images. The
newly proposed method is based on a convolutional neural network algorithm for
semantic segmentation. The proposed method is extensively tested and evaluated
against two ceramic material systems ($Al_2O_3$,$MgAl_2O_4$) and shows high
prediction accuracy, despite being trained on only one material system
($MgAl_2O_4$). While here attention is focused on brittle fracture
characteristics, the method can be easily extended to account for other
fracture morphologies, such as dimples, fatigue striations, etc.
