  Segmentation of distinct bones plays a crucial role in diagnosis, planning,
navigation, and the assessment of bone metastasis. It supplies semantic
knowledge to visualisation tools for the planning of surgical interventions and
the education of health professionals. Fully supervised segmentation of 3D data
using Deep Learning methods has been extensively studied for many tasks but is
usually restricted to distinguishing only a handful of classes. With 125
distinct bones, our case includes many more labels than typical 3D segmentation
tasks. For this reason, the direct adaptation of most established methods is
not possible. This paper discusses the intricacies of training a 3D
segmentation network in a many-label setting and shows necessary modifications
in network architecture, loss function, and data augmentation. As a result, we
demonstrate the robustness of our method by automatically segmenting over one
hundred distinct bones simultaneously in an end-to-end learnt fashion from a
CT-scan.
