Multi-dimensional deep learning–based segmentation and volumetric assessment of sphenoid sinus fluid on postmortem CT in drowning cases

Abstract Sphenoid sinus fluid is considered a supportive indicator of drowning in forensic medicine, but traditional manual assessment on postmortem computed tomography (PMCT) is labor-intensive and observer-dependent. Efficient, reproducible methods for quantitative evaluation are needed in forensic practice. This study developed deep learning–based approaches for the automated segmentation and volumetric estimation of sphenoid sinus fluid using PMCT images from 165 autopsy-confirmed drowning cases. Three U-Net–based models (2D, 2.5D, and 3D) were developed and evaluated against manually annotated reference standards. In the test dataset, mean Dice coefficients were 0.866 (2D), 0.869 (2.5D), and 0.798 (3D). Volumetric estimates showed no statistically significant differences from the reference standard, with strong correlations (Spearman’s ρ = 0.976–0.988). Mean absolute errors were 0.218 (2D), 0.206 (2.5D), and 0.310 ml (3D). The 2.5D approach provided the most balanced performance between segmentation accuracy and volumetric estimation. These findings demonstrate the feasibility of automated PMCT-based segmentation and volumetric quantification of sphenoid sinus fluid, enabling quantitative assessment on PMCT images prior to autopsy.