On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation
Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learning architectures such as U-Net and its variants, performance degradation across datasets remains a major challenge, particularly under domain shift and limited annotated data. To address this issue, this study systematically evaluates how individual MRI sequences influence model robustness across two well-known datasets. A ResUNet-based framework is employed, where each modality is trained independently to isolate its effect under a controlled cross-dataset evaluation protocol with tumor size stratification, without target-domain training,...
| Authors | Henrique Zan Grande, João G. Pitol, Lucas B. Schuck et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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