  Bone segmentation from CT images is a task that has been worked on for
decades. It is an important ingredient to several diagnostics or treatment
planning approaches and relevant to various diseases. As high-quality manual
and semi-automatic bone segmentation is very time-consuming, a reliable and
fully automatic approach would be of great interest in many scenarios. In this
publication, we propose a UNet inspired architecture to address the task using
Deep Learning. We evaluated the approach on whole-body CT scans of patients
suffering from multiple myeloma. As the disease decomposes the bone, an
accurate segmentation is of utmost importance for the evaluation of bone
density, disease staging and localization of focal lesions. The method was
evaluated on an in-house data-set of 6000 2D image slices taken from 15
whole-body CT scans, achieving a dice score of 0.96 and an IOU of 0.94.
