On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2608.29944v1
• Date: Sun, 30 Aug 2026 18:17:18 GMT
• Title: On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation
• Abstract summary: This study systematically evaluates how individual MRI sequences influence model robustness across two well-known datasets.<n>A ResUNet-based framework is employed, where each modality is trained independently to isolate its effect.<n>Results show that the T2f/FLAIR sequence achieves the best cross-dataset performance, with Dice scores exceeding 75%.
• Score: 0.6303112417588329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, or with limited domain adaptation. Results show that the T2f/FLAIR sequence achieves the best cross-dataset performance, with Dice scores exceeding 75%. It consistently outperforms other modalities across most tumor size ranges, while multi-sequence training further improves performance. Additionally, even limited target-domain adaptation yields rapid initial gains, reducing the need for extensive annotations and costly retraining. Our source code is publicly available at https://github.com/henrique-zan/brain_tumor_segmentation/.

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