一种可泛化的头部MRI去脸处理流水线：在2566例脑膜瘤扫描影像上的评估
A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans
AI总结：
针对现有MRI去脸方法存在的去脸不彻底、脑组织退化问题，提出融合基于图谱的配准与脑掩码的高分辨率MRI去脸流水线，在2566例脑膜瘤扫描上验证了高成功率与优异的解剖结构保留效果。
摘要：
可靠的MRI去脸技术可在保护患者隐私的同时保留脑部解剖结构，对研究合作至关重要。现有方法常存在去脸不彻底或脑组织区域画质退化的问题。我们提出了一种稳健、可泛化的高分辨率MRI去脸处理流水线，将基于图谱的配准与脑掩码提取相结合。我们在2566例异质性脑膜瘤临床扫描影像上对该方法进行了评估，经目视检查成功率达99.92%（2564/2566）。从原始影像和去脸后影像中自动提取的脑掩码之间的Dice相似系数为0.9975±0.0023，证明其解剖结构保留效果优异。源代码可在https://github.com/cai4cai/defacing_pipeline获取。
英文摘要：
Reliable MRI defacing techniques to safeguard patient privacy while preserving brain anatomy are critical for research collaboration. Existing methods often struggle with incomplete defacing or degradation of brain tissue regions. We present a robust, generalisable defacing pipeline for high-resolution MRI that integrates atlas-based registration with brain masking. Our method was evaluated on 2,566 heterogeneous clinical scans for meningioma and achieved a 99.92 per cent success rate (2,564/2,566) upon visual inspection. Excellent anatomical preservation is demonstrated with a Dice similarity coefficient of 0.9975 plus or minus 0.0023 between brain masks automatically extracted from the original and defaced volumes. Source code is available at https://github.com/cai4cai/defacing_pipeline.
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