Enhancing Generalizability in Brain Tumor Segmentation: Model Ensemble with Adaptive Post-Processing

Segmentation of brain tumors in multi-parametric magnetic resonance imaging facilitates quantitative analysis crucial for clinical trials and personalized patient care. This significantly influences clinical decision-making, encompassing diagnosis and prognosis and enhancing patient outcomes. The brain tumor segmentation (BraTS) challenge, in its 2023 edition, extended to a cluster of competitions incorporating multiple tumor types. Now, in conjunction with IEEE ISBI 2024, BraTS organizes its Generalizability Across Tumors (BraTS-GoAT) challenge. In this paper, we introduce a deep-learning-based ensemble strategy involving three state-of-the-art segmentation models. Furthermore, we also introduce a novel adaptive post-processing method, based on a cross-validated tumor-specific threshold search, designed to output enhanced accurate segmentations, ensuring generalizability across various tumor types. The evaluation of our proposed method on validation cases resulted in lesionwise Dice scores of 0.842, 0.854, 0.872 and lesion-wise 95th-percentile Hausdorff Distance scores of 29.46, 24.67, 25.22 for the enhancing tumor, tumor core, and whole tumor, respectively.