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 ( 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 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.