  Image-based algorithmic software segmentation is an increasingly important
topic in many medical fields. Algorithmic segmentation is used for medical
three-dimensional visualization, diagnosis or treatment support, especially in
complex medical cases. However, accessible medical databases are limited, and
valid medical ground truth databases for the evaluation of algorithms are rare
and usually comprise only a few images. Inaccuracy or invalidity of medical
ground truth data and image-based artefacts also limit the creation of such
databases, which is especially relevant for CT data sets of the
maxillomandibular complex. This contribution provides a unique and accessible
data set of the complete mandible, including 20 valid ground truth segmentation
models originating from 10 CT scans from clinical practice without artefacts or
faulty slices. From each CT scan, two 3D ground truth models were created by
clinical experts through independent manual slice-by-slice segmentation, and
the models were statistically compared to prove their validity. These data
could be used to conduct serial image studies of the human mandible, evaluating
segmentation algorithms and developing adequate image tools.
