To develop and test a method for fully automated segmentation of bony structures from whole-body CT and evaluate its performance compared to manual segmentation METHODS AND MATERIALS;: We developed a workflow for automatic whole-body bone segmentation using atlas-based segmentation method with a post-processing module (ABSPP) in MIM MAESTRO software. 52 CT scans comprised the training set to build the atlas library and 29 CT scans comprised the test set. To validate the workflow, we compared Dice similarity coefficient (DSC), mean distance to agreement (MDA), and relative volume errors (ΔV) between ABSPP and atlas-based segmentation with no post-processing (ABSNPP), with manual segmentation as the reference (gold standard). RESULTS;: The ABSPP method resulted in significantly improved segmentation accuracy (DSC range: 0.85 to 0.98) compared to the ABSNPP method (DSC range: 0.55 to 0.87) (p<0.001). MDA results also indicated high agreement between ABSPP and manual reference delineations (range: 0.11-1.56 mm), which was significantly improved compared to ABSNPP (range: 1.00-2.34 mm) for for the majority of tested bony structures. Relative volume errors were also significantly lower for ABSPP compared to ABSNPP for most bony structures. We developed a fully automated MIM workflow for bony structure segmentation from whole-body CT, which exhibited high accuracy compared to manual delineation. The integrated post-processing module significantly improved workflow performance.