Deep learning segmentation with uncertainty quantification for spinal tuberculosis on fat-suppressed T2-weighted MRI.

BACKGROUND: Spinal tuberculosis is the most common extrapulmonary manifestation of tuberculosis. Magnetic resonance imaging (MRI), particularly fat-suppressed T2-weighted imaging (FS-T2WI), is the modality of choice for preoperative evaluation; yet indistinct lesion boundaries render manual delineation subjective and poorly reproducible. Although deep learning segmentation has advanced considerably, its "black-box" nature and limited interpretability remain critical obstacles. This study aimed to develop an uncertainty-guided deep learning segmentation framework and evaluate its accuracy and clinical utility. METHODS: We retrospectively enrolled 210 patients with spinal tuberculosis from an initial cohort of 300 screened at three centers, and acquired preoperative FS-T2WI scans. Data from Center 1 (n = 160) were used for five-fold cross-validation, while the remaining 50 cases served as an external test set. We built an improved model on nnU-Net by integrating boundary-aware loss with Monte Carlo Dropout, and compared it against U-Net, Attention U-Net, and TransUNet. The uncertainty threshold was determined through internal cross-validation, and clinical validation followed a within-subject crossover design involving nine physicians. RESULTS: On the external test set, the improved model achieved a Dice similarity coefficient of 0.858 and an AUC of 0.912, outperforming all comparative models. Uncertainty correlated strongly and positively with pixel-level error rates (Spearman ρ = 0.74). At a threshold of 0.52, sensitivity for identifying unreliable segmentation was 84.3% and negative predictive value was 89.5%. In the physician validation, overlaying uncertainty maps significantly increased trust scores (3.8 ± 0.7 vs. 3.2 ± 0.8; P < 0.001, Cohen's d = 0.80) and reduced review time (P = 0.018), with the greatest benefit observed among resident physicians. In cases of obvious AI failure, uncertainty alerts accurately flagged erroneous regions in 80.0% of instances. CONCLUSION: The proposed uncertainty-guided framework improved spinal tuberculosis lesion segmentation accuracy. Pixel-level uncertainty maps reliably identified unreliable predictions and enhanced clinician trust, offering a robust AI decision-support tool for precise surgical planning.