Accurate trachea segmentation from computed to- mography (CT) is a prerequisite for image-guided airway assess- ment, precision tracheostomy planning, and safe endotracheal tube placement. The trachea presents distinct segmentation challenges due to its elongated morphology, small cross-sectional area, sensitivity to partial-volume effects, motion artifacts, and heterogeneous surrounding mediastinal structures. This study systematically compares two complementary paradigms: a fully automatic self-configuring framework (nnU-Net) and a prompt- conditioned foundation model (MedSAM) derived from the Seg- ment Anything Model (SAM). Evaluation is performed under heterogeneous dataset regimes including volumetric CT data with consistent inter-slice continuity and slice-based CT data lacking reliable volumetric structure. A hybrid inference strategy enabling automatic prompt generation is introduced. Quantita- tive and qualitative analyses demonstrate that dataset structure critically influences segmentation reliability, boundary stability, and deployment feasibility in precision airway workflows.