  Purpose: The localisation and segmentation of individual bones is an
important preprocessing step in many planning and navigation applications. It
is, however, a time-consuming and repetitive task if done manually. This is
true not only for clinical practice but also for the acquisition of training
data. We therefore not only present an end-to-end learnt algorithm that is
capable of segmenting 125 distinct bones in an upper-body CT, but also provide
an ensemble-based uncertainty measure that helps to single out scans to enlarge
the training dataset with. Methods We create fully automated end-to-end learnt
segmentations using a neural network architecture inspired by the 3D-Unet and
fully supervised training. The results are improved using ensembles and
inference-time augmentation. We examine the relationship of
ensemble-uncertainty to an unlabelled scan's prospective usefulness as part of
the training dataset. Results: Our methods are evaluated on an in-house dataset
of 16 upper-body CT scans with a resolution of \SI{2}{\milli\meter} per
dimension. Taking into account all 125 bones in our label set, our most
successful ensemble achieves a median dice score coefficient of 0.83. We find a
lack of correlation between a scan's ensemble uncertainty and its prospective
influence on the accuracies achieved within an enlarged training set. At the
same time, we show that the ensemble uncertainty correlates to the number of
voxels that need manual correction after an initial automated segmentation,
thus minimising the time required to finalise a new ground truth segmentation.
Conclusion: In combination, scans with low ensemble uncertainty need less
annotator time while yielding similar future DSC improvements. They are thus
ideal candidates to enlarge a training set for upper-body distinct bone
segmentation from CT scans. }
