ArticleFrontiers in neuroscience2026
Uncertainty-aware feature mapping and adaptive inference for brain tumor segmentation with missing contrast-enhanced T1-weighted imaging.
Article in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Multi-parametric magnetic resonance imaging (mpMRI) is a cornerstone of clinical brain tumor assessment, yet the contrast-enhanced T1-weighted (T1ce) sequence is frequently unavailable due to contrast-agent contraindications or acquisition failures. Existing segmentation methods degrade substantially when key modalities are missing, motivating the need for robust missing-modality segmentation frameworks. Methods: We propose UAF-AIMM (Uncertainty-Aware Feature Mapping and Adaptive Inference for Brain Tumor Segmentation with Missing Contrast-Enhanced T1-Weighted Imaging), a 3D brain tumor segmentation framework for the clinically prevalent missing-T1ce scenario. UAF-AIMM integrates three components: (1) MR-Mapper, a cross-modal feature mapping module that recovers T1ce-related semantics in the latent space from observed sequences; (2) Uncertainty-Aware Attention (UAA), which estimates voxel-wise predictive variance and uses learnable gating to fuse features according to their estimated reliability; and (3) Single-Image Test-Time Adaptation (SITA), a per-case calibration strategy that updates only normalization affine parameters to mitigate domain shift at inference. Results: Evaluated on the BraTS (Brain Tumor Segmentation) 2021 dataset under the missing-T1ce protocol, UAF-AIMM achieves an average Dice similarity coefficient of 89.20% and a 95th percentile Hausdorff distance (HD95) of 5.50 mm, outperforming representative baselines including 3D U-Net, Swin UNETR, mmFormer, and ReCoSeg. The improvement is most pronounced in the contrast-dependent enhancing tumor (ET) subregion. Conclusion: The results demonstrate that feature-space compensation with explicit reliability control and test-time calibration provides a practical and effective direction for robust brain tumor segmentation under incomplete multi-modal MRI protocols, with potential for translation to heterogeneous clinical environments.
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