用置信度方法提升脑胶质瘤分割可靠性，让模型自知不确定

Background and Purpose: Glioma segmentation is crucial for clinical decisions and treatment planning. Uncertainty quantification methods, including conformal prediction (CP), can enhance segmentation models reliability. This study aims to use CP in glioma segmentation. Methods: We used the UCSF and UPenn glioma datasets, with the UCSF dataset split into training (70%), validation (10%), calibration (10%), and test (10%) sets, and the UPenn dataset divided into external calibration (30%) and external test (70%) sets. A UNet model was trained, and its optimal threshold was set to 0.5 using prediction normalization. To apply CP, the conformal threshold was selected based on the internal/external calibration nonconformity score, and CP was subsequently applied to the internal/external test sets, with coverage reported for all. We defined the uncertainty ratio (UR) and assessed its correlation with the Dice score coefficient (DSC). Additionally, we categorized cases into certain and uncertain groups based on UR and compared their DSC. We also evaluate the correlation between UR and DSC of the BraTS fusion model segmentation (BFMS), and compare DSC in the certain and uncertain subgroups. Results: The base model achieved a DSC of 0.8628 and 0.8257 on the internal and external test sets, respectively. The CP coverage was 0.9982 for the internal test set and 0.9977 for the external test set. Statistical analysis showed a significant negative correlation between UR and DSC for test sets (p<0.001). UR was also linked to significantly lower DSCs in the BFMS (p<0.001). Additionally, certain cases had significantly higher DSCs than uncertain cases in test sets and the BFMS (p<0.001). Conclusion: CP effectively quantifies uncertainty in glioma segmentation. Using CONSeg improves the reliability of segmentation models and enhances human-computer interaction.