  Crystalline defects, such as line-like dislocations, play an important role
for the performance and reliability of many metallic devices. Their interaction
and evolution still poses a multitude of open questions to materials science
and materials physics. In-situ TEM experiments can provide important insights
into how dislocations behave and move. During such experiments, the dislocation
microstructure is captured in form of videos. The analysis of individual video
frames can provide useful insights but is limited by the capabilities of
automated identification, digitization, and quantitative extraction of the
dislocations as curved objects. The vast amount of data also makes manual
annotation very time consuming, thereby limiting the use of Deep
Learning-based, automated image analysis and segmentation of the dislocation
microstructure. In this work, a parametric model for generating synthetic
training data for segmentation of dislocations is developed. Even though domain
scientists might dismiss synthetic training images sometimes as too artificial,
our findings show that they can result in superior performance, particularly
regarding the generalizing of the Deep Learning models with respect to
different microstructures and imaging conditions. Additionally, we propose an
enhanced deep learning method optimized for segmenting overlapping or
intersecting dislocation lines. Upon testing this framework on four distinct
real datasets, we find that our synthetic training data are able to yield
high-quality results also on real images-even more so if fine-tune on a few
real images was done.
