Within the scope of this study a methodology for the semi-supervised training of deep learning models for fracture surfaces segmentation on a macroscopic level with foto images was established. Three distinct and unique datasets were created according to the structural similarity of the images. On the heterogeneous dataset we were able to train robust and well-generalizing models that learned feature representations from images across different domains without observing a significant drop in prediction quality. Furthermore, our approach reduced the number of labeled images required for training by a factor of 6. To demonstrate the success of our method and the benefit of our approach for the fracture mechanics assessment, we utilized the models for initial crack size measurements with the area average method. For the laboratory setting, the deep learning assisted measurements proved to have the same quality as manual measurements. For models trained on the heterogeneous dataset, very good measurement accuracies with mean deviations smaller than 1 % could be achieved, showcasing the enormous potential of our approach.