MRI序列在脑肿瘤分割跨数据集泛化中的作用
On the Role of MRI Sequences in Cross-Dataset Generalization for Brain Tumor Segmentation
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中文总结 AI 辅助
本研究评估MRI序列对脑肿瘤分割跨数据集泛化的影响，采用ResUNet框架，发现T2f/FLAIR序列跨数据集性能最优，多序列训练可进一步提升效果，有限目标域适应能减少标注需求。
中文摘要 AI 辅助
磁共振成像（MRI）中的脑肿瘤分割是诊断和治疗规划的关键任务。尽管U-Net及其变体等深度学习架构已取得成功，但跨数据集的性能下降仍是主要挑战，尤其在域偏移和标注数据有限的情况下。为解决该问题，本研究系统评估了单个MRI序列如何影响模型在两个知名数据集间的鲁棒性。采用基于ResUNet的框架，在受控的跨数据集评估协议（按肿瘤大小分层、无目标域训练或有限域适应）下，对每个模态独立训练以分离其影响。结果显示，T2f/FLAIR序列实现了最佳跨数据集性能，Dice分数超过75%；在大多数肿瘤大小范围内，其始终优于其他模态，而多序列训练进一步提升性能。此外，即使有限的目标域适应也能快速获得初始增益，减少了对大量标注和昂贵重训练的需求。本研究的源代码公开于此https URL。
英文摘要
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/.
发表机构
- Pontifical Catholic University of Paraná（巴拉那州天主教大学）
机构由 AI 辅助整理，请以论文原文为准。
