Localization-guided segmentation of Gliomas via 3D ReFusionNet

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Purpose This study presents a localization-guided two-stage 3D framework for the automated segmentation of brain tumors. Methods ReFusionNet first estimates a single region of interest containing the tumor-bearing region and then segments the whole tumor, tumor core, and enhancing tumor within the localized volume. The framework was evaluated on the UPENN-GBM and BraTS 2021 datasets using recall, precision, Dice score, and the 95th-percentile Hausdorff distance (HD95). Results On the UPENN-GBM test set, ReFusionNet-A achieved Dice scores of 0.90, 0.81, and 0.77 for the whole tumor, tumor core, and enhancing tumor, respectively. Its corresponding HD95 values were 11.68, 21.53, and 10.49. The localization-guided models generally showed higher precision and lower HD95 values for selected regions, but lower recall and Dice scores occurred in some tumor-core and enhancing-tumor comparisons. On BraTS 2021, ReFusionNet-C achieved competitive Dice scores, although its HD95 values were not the lowest among the models compared. Conclusion ReFusionNet achieved competitive segmentation performance on the evaluated public datasets. Its principal observed advantages were higher precision and lower HD95 values in selected tumor regions, while performance gains were not uniform across all metrics.