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

2026-08-30

AI Summary

研究通过独立训练不同MRI序列，采用ResUNet框架评估了各序列对跨数据集脑肿瘤分割模型鲁棒性的影响，发现T2f/FLAIR序列表现最佳。

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/.

Problem

• MRI sequences
• cross-dataset generalization
• brain tumor segmentation
• domain shift
• limited annotated data

Innovation

• MRI sequences
• cross-dataset generalization
• ResUNet
• T2f/FLAIR
• limited domain adaptation