用于3D膝关节MRI分割和跨域泛化性的强度归一化方法的系统基准测试
A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisability
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中文总结 AI 辅助
研究针对膝关节MRI分割，系统比较七种强度归一化方法对3D U-Net模型性能的影响，用IWOAI 2019数据集训练并在内外测试集评估，发现归一化对泛化性有影响但相对域偏移有限，凸显需互补策略。
中文摘要 AI 辅助
强大的开箱即用性能对于深度学习模型在医学成像中的临床应用至关重要。影响模型泛化性的一个重要但未充分探索的因素是强度归一化，特别是在磁共振成像（MRI）中，图像强度会因扫描仪和协议而异。在本研究中，我们系统地比较了七种归一化方法及其对用于从膝关节MRI分割半月板的3D U-Net模型性能的影响。这些方法包括标准缩放方法、基于直方图的技术以及基于高斯混合模型（GMM）的方法。模型在IWOAI 2019数据集上进行训练，并在内部和外部测试集（SKM-TEA）上进行评估以评估泛化性。内部性能相似，但外部数据上差异显著，Z分数、Nyúl直方图匹配和CLAHE比其他方法表现出更强的鲁棒性。然而，与数据集之间观察到的显著性能下降相比，这些差异较小。总体而言，虽然强度归一化对模型泛化性有可测量的影响，但其影响相对于域偏移的影响是有限的，这突出了需要互补策略以实现稳健部署。
英文摘要
Robust out-of-the-box performance is essential for the clinical deployment of deep learning models in medical imaging. An important but underexplored factor affecting model generalisability is intensity normalisation, particularly for magnetic resonance imaging (MRI), where image intensities vary across scanners and protocols. In this study, we systematically compared seven normalisation methods and their impact on the performance of a 3D U-Net model for meniscus segmentation from knee MRI. The methods included standard scaling approaches, histogram-based techniques, and a Gaussian Mixture Model (GMM)-based method. Models were trained on the IWOAI 2019 dataset and evaluated on both internal and external test sets (SKM-TEA) to assess generalisability. Performance was similar internally but differences were significant on external data, with Z-score, Nyúl histogram matching, and CLAHE showing greater robustness than other methods. However, these differences were small compared to the significant performance drop observed between datasets. Overall, while intensity normalisation had a measurable effect on model generalisability, its impact was limited relative to the effects of domain shift, highlighting the need for complementary strategies for robust deployment.
发表机构
- University of Leeds（利兹大学）
- NIHR Leeds Biomedical Research Centre（英国国家卫生研究院利兹生物医学研究中心）
机构由 AI 辅助整理，请以论文原文为准。
