  Concrete is the standard construction material for buildings, bridges, and
roads. As safety plays a central role in the design, monitoring, and
maintenance of such constructions, it is important to understand the cracking
behavior of concrete. Computed tomography captures the microstructure of
building materials and allows to study crack initiation and propagation. Manual
segmentation of crack surfaces in large 3d images is not feasible. In this
paper, automatic crack segmentation methods for 3d images are reviewed and
compared. Classical image processing methods (edge detection filters, template
matching, minimal path and region growing algorithms) and learning methods
(convolutional neural networks, random forests) are considered and tested on
semi-synthetic 3d images. Their performance strongly depends on parameter
selection which should be adapted to the grayvalue distribution of the images
and the geometric properties of the concrete. In general, the learning methods
perform best, in particular for thin cracks and low grayvalue contrast.
