Accurate segmentation of structures in CT is essential for clinical tasks such as tumour staging, radiotherapy planning, fracture assessment, and monitoring of disease progression. Current deep learning-based automated "segmentators" face challenges due to variability in scanner parameters, anatomical regions, and training data, which impact performance consistency across diverse datasets. We evaluated various total body segmentators on publicly available lung CT data excluded from their training sets. We found that these segmentators exhibit label mixing within individual ribs and vertebrae, often requiring anatomy-informed post-processing steps to improve accuracy. Combining multiple models and incorporating anatomical information enhances segmentation outcomes compared to using single models, highlighting the complementary strengths of different segmentation approaches and task-dependent a priori knowledge.