This contribution presents the automatic segmentation of the lower jawbone (mandible) in humans' computed tomography (CT) images with the support of trained deep learning networks. CT acquisitions from the mandible frequently include radiological artifacts e.g., from metal dental restorations, ostheosynthesis materials or include trauma related free pieces of bones with missing bone contour anatomy. As a result, manual outlining these slices to generate the ground truth for evaluating segmentation algorithms lead to massive uncertainties and results in significant interphysician disagreement. Simply excluding these slices is also not the option of choice, regarding the treatment outcome. Hence, we defined strict inclusion and exclusion criteria for our datasets to avoid subjectivity or occurring bias in the groundtruth creation. Amongst others, datasets must display a complete physiological mandible without teeth. According to these data selection criteria such images are difficult to find since they originate from the clinical routine and therefore need a medical indication (such as trauma or pathologic lesions) to be provided as CT data. Furthermore, to prove the adequateness of our ground-truth, clinical experts segmented all cases twice manually, showing the great qualitative and quantitative agreement between them. Our dataset collection and the corresponding ground truth is an absolute novelty and the first serious evaluation of segmentation algorithms for the mandible.