Whole body diffusion-weighted MR imaging is a promising technique for the evaluation of bone metastases e.g. in prostate and breast cancer. Segmenting and quantifying tumor burden and treatment response based on DWI and corresponding ADC has been proposed previously. However, treatment effects may influence the actual segmentation and signal intensities. Here, a more reproducible approach would be preferred for segmenting and analyzing consistent regions of interest also in follow-up examinations. We present a deep learning method for automatic bone segmentation, based on T1-weighted imaging or DWI. Feasibility of the new approach is shown, resulting in significantly reduction in computation time.