Clinician-Guided Deep Learning Segmentation of Skull Base Pneumatization on Computed Tomography Using 3D Slicer and MONAI Label

Skull base pneumatization is anatomically variable and clinically relevant to temporal bone and transsphenoidal surgical corridors, but manual volumetric segmentation is time-consuming. This retrospective pilot study evaluated a clinician-guided deep learning workflow for mastoid and sphenoid sinus compartment segmentation on bone computed tomography. Images were curated and annotated in 3D Slicer using MONAI Label and separate three-dimensional SegResNet models. The mastoid development dataset comprised 122 side-cases, with 28 reserved side-cases; the sphenoid dataset comprised 117 development and 17 reserved examinations. The best internal-validation Dice scores were 0.8660 for the mastoid model and 0.8750 for the sphenoid model. In AI-assisted correction cohorts, mean Dice ranged from 0.9539 to 0.9691 for mastoid and from 0.9347 to 0.9426 for sphenoid. In independently annotated subsets, AI-to-expert Dice was 0.8083–0.8097 for mastoid and 0.8339–0.8473 for sphenoid, while interobserver Dice was 0.8003 and 0.9136, respectively. AI assistance reduced mean segmentation time by 86.5% for mastoid and 81.4% for sphenoid. These findings support clinician-supervised AI segmentation as an efficient starting point for volumetric assessment.