Dynamic expert routing with boundary-aware decoding for accurate brain tumor segmentation from incomplete MRI data.

Precise segmentation of brain tumors from multi-modal MRI scans is critical to clinical diagnosis and therapeutic planning, but real images from MRI machines are often lacking in some modalities due to variability in acquisition procedures or clinical limitations. This study proposes a modality-agnostic approach to segmentation of tumors from incomplete MRI scans and ultimately creates a framework that effectively supports the robust segmentation of tumors in incomplete input. The framework employs adaptive expert routing combined with low-rank adaptation and cross-modality consistency regularization as well as uncertainty-aware decoding and boundary-guided curriculum training to achieve a unified architecture for the same purpose. The effectiveness of this framework was determined using both BraTS2019 and BraTS2020 benchmark data, where the twin scores for BraTS2019 result in (WT = 0.918/TC = 0.889/ET = 0.852) and (ii) BraTS2020 result in (WT = 0.924 and/TC = 0.894 and/ET = 0.861) for a total of eight cases (WT = 4 and TC = 3 and ET = 1) with full modality input for both BraTS2019 and BraTS2020. Importantly, regardless of whether individual or multiple modalities were removed, all cases exhibited continued robustness through a managed degree of decline in performance. Additionally, compared to both benchmark and non-benchmark protocols, our framework exhibited improved boundary alignment characteristics and statistically significant increases in performance relative to traditional/comparative architectures while maintaining competitive computational efficiency. Thus these results suggest that explicitly modeling the availability of modality representations during the learning of a representation provides a practical means to produce accurate segmentation results for a wide variety of tumors from MRI images in high degree heterogeneous clinical diagnostic settings.