  Segmentation of bone regions allows for enhanced diagnostics, disease
characterisation and treatment monitoring in CT imaging. In contrast enhanced
whole-body scans accurate automatic segmentation is particularly difficult as
low dose whole body protocols reduce image quality and make contrast enhanced
regions more difficult to separate when relying on differences in pixel
intensities. This paper outlines a U-net architecture with novel preprocessing
techniques, based on the windowing of training data and the modification of
sigmoid activation threshold selection to successfully segment bone-bone marrow
regions from low dose contrast enhanced whole-body CT scans. The proposed
method achieved mean Dice coefficients of 0.979, 0.965, and 0.934 on two
internal datasets and one external test dataset respectively. We have
demonstrated that appropriate preprocessing is important for differentiating
between bone and contrast dye, and that excellent results can be achieved with
limited data.
