NeuroWeave-3D: Uncertainty-Aware Evidence Completion for Brain Tumor Segmentation with Missing MRI Modalities

Abstract

Multi-parametric MRI segmentation models are accurate when all prescribed sequences are available but can be brittle under missing or corrupted modalities. We propose NeuroWeave-3D, a 3D glioma segmentation framework that treats missing modalities as uncertain evidence rather than deterministic channels to reconstruct. The model combines spatial modality routing, probabilistic latent feature completion, complete-to-missing consistency, and voxel-level uncertainty estimation. A curriculum over all non-empty MRI modality subsets trains one network for arbitrary sequence availability. We define a BraTS-centered evaluation that reports complete-input accuracy, macro and worst-case missing-modality performance, corruption robustness, calibration, and failure detection.