Counterfactual Context Auditing for Glioma Segmentation Failure Detection Across MRI Datasets

Abstract

Glioma segmentation models can lose accuracy across MRI datasets, while identifying the cases that require correction remains difficult. In this study, we introduce counterfactual context transport (CCT), which replaces a scan's feature statistics with those from training scans and measures the resulting changes in tumor masks. We combine this sensitivity with prediction uncertainty to rank cases for review without retraining or additional segmentation networks. Experiments on BraTS and UTSW show that context sensitivity provides evidence of segmentation failure that uncertainty in the original prediction alone does not consistently capture. Combining both signals gives the strongest average failure detection among the evaluated methods, including an ensemble of separately trained networks. These findings support using changes in tumor predictions under context replacement to help identify cases that require expert review.

arXiv comment4 pages, 3 figures