T2f/FLAIR序列在脑肿瘤分割中跨数据集表现最佳，提升模型泛化能力。
On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation
- 分别训练各MRI序列，隔离其对跨数据集性能的影响
- T2f/FLAIR序列在多数肿瘤尺寸下Dice超75%，表现最优
- 少量目标域适应即可快速提升性能，减少标注成本
磁共振成像（MRI）中的脑肿瘤分割对诊断与治疗规划至关重要。尽管U-Net及其变体等深度学习架构取得成功，但在不同数据集间仍面临性能下降问题，尤其在领域偏移和标注数据有限的情况下。本研究系统评估了各MRI序列对模型跨数据集鲁棒性的影响。采用ResUNet框架，每个模态独立训练，通过控制变量的跨数据集评估协议进行测试，不使用目标域训练或仅用少量领域自适应。结果表明，T2f/FLAIR序列在跨数据集任务中表现最佳，Dice分数超过75%。该序列在多数肿瘤尺寸范围内均优于其他模态，多序列联合训练进一步提升性能。此外，即使仅进行有限的目标域适应，也能快速获得显著增益，降低对大量标注数据和昂贵重训练的需求。源代码已公开于https://github.com/henrique-zan/brain_tumor_segmentation/。
原文摘要 · Abstract (English)
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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