Conjugate Bayesian Evidential Learning for Uncertainty-Aware Nasopharyngeal Carcinoma Segmentation

Accurate automatic segmentation of the gross tumor volume (GTV) in nasopharyngeal carcinoma (NPC) is critical for radiotherapy planning, yet conventional deterministic models do not adequately characterize the inherent uncertainty associated with ambiguous tumor boundaries on magnetic resonance imaging (MRI). Existing evidential deep learning (EDL) methods for uncertainty quantification typically rely on fixed priors and are theoretically prone to evidence saturation, thereby limiting the reliability of uncertainty estimation. To address these issues, we propose a conjugate Bayesian evidential segmentation (CoBESeg) framework for uncertainty-aware NPC GTV segmentation. To alleviate evidence saturation in conventional EDL, a hierarchical variance evidence network is introduced to explicitly integrate anatomical prior knowledge with image-driven evidence through conjugate Bayesian updating. To improve feature discrimination in ambiguous boundary regions, a fuzzy prototype contrastive learning strategy is proposed to enhance the discriminability of evidential representations. Furthermore, CoBESeg incorporates a feature distance-aware evidential calibration strategy to dynamically calibrate evidence strength at test time. Experiments on three multicenter NPC MRI datasets demonstrate that CoBESeg achieves superior segmentation accuracy and more reliable uncertainty estimation compared with state-of-the-art methods, while also supporting risk filtering under clinical shift. The code is available at https://github.com/zhangchi73/hvenfpcl.