AI 中文总结
该研究针对MRI的隐私问题，提出一种基于深度学习脑提取的可配置隐私保护MRI处理工作流，具备自适应解剖结构保留、交互式选择与集成质控功能，为隐私导向神经影像研究提供实用框架。
AI 中文摘要
结构磁共振成像（MRI）广泛应用于神经影像学研究和临床实践，但结构MRI体积可能保留面部和颅骨解剖信息，引发隐私担忧。现有基于深度学习的脑提取方法通常产生单一固定输出，当不同应用需要在隐私和解剖结构保留之间进行不同权衡时，灵活性受限。本文提出一种可配置的隐私保护MRI处理工作流，该工作流通过自适应解剖结构保留、交互式保留选择和集成质量控制扩展基于深度学习的脑提取。该工作流采用SynthStrip进行自动脑提取，随后进行形态学掩码扩展以生成可配置的基于壳层的保留级别。交互式保留框架使用户能够比较保留配置并选择合适的输出，而集成质量控制框架提供多平面可视化和脑掩码叠加验证。该工作流在Renku可重复研究环境中使用Python和开源神经影像库实现，并使用来自公开IXI数据集的结构T1加权MRI数据进行评估。实验结果表明，该工作流实现了解剖学上合理的脑提取和可配置的保留输出，且得到系统视觉验证。主要贡献是提出了一种模块化且可重复的MRI预处理框架，该框架通过可配置的解剖结构保留、交互式用户引导处理和集成质量控制增强了基于深度学习的脑提取，为面向隐私的神经影像学研究和协作医学图像分析提供了实用基础。
英文摘要
Structural Magnetic Resonance Imaging (MRI) is widely used in neuroimaging research and clinical practice, but structural MRI volumes may retain facial and cranial anatomical information that raises privacy concerns. Existing deep learning-based brain extraction methods generally produce a single fixed output, limiting flexibility when different applications require different balances between privacy and anatomical preservation. This paper presents a configurable privacy-preserving MRI processing workflow that extends deep learning-based brain extraction through adaptive anatomical preservation, interactive preservation selection, and integrated quality control. The workflow employs SynthStrip for automated brain extraction, followed by morphological mask expansion to generate configurable shell-based preservation levels. An Interactive Preservation Framework enables users to compare preservation configurations and select an appropriate output, while an integrated Quality Control Framework provides multi-plane visualisation and brain-mask overlay verification. The workflow was implemented in Python using open-source neuroimaging libraries within the Renku reproducible research environment and evaluated using structural T1-weighted MRI data from the publicly available IXI dataset. Experimental results demonstrate anatomically plausible brain extraction and configurable preservation outputs, supported by systematic visual verification. The principal contribution is a modular and reproducible MRI preprocessing framework that enhances deep learning-based brain extraction with configurable anatomical preservation, interactive user-guided processing, and integrated quality control. The workflow provides a practical foundation for privacy-oriented neuroimaging research and collaborative medical image analysis.
Comments10 pages, 6 figures
