可调节隐私保护的脑部MRI处理流程，兼顾隐私与解剖保真度。
A Configurable Privacy-Preserving MRI Processing Workflow Using Deep Learning-Based Brain Extraction and Adaptive Anatomical Preservation
- 通过可配置壳层扩展实现不同隐私等级的解剖结构保留
- 支持交互式选择并可视化验证，确保输出符合需求
- 开源可复现，适合注重隐私的神经影像研究与协作
结构磁共振成像（MRI）在神经影像研究和临床中广泛应用，但其数据可能包含面部和颅骨解剖信息，引发隐私担忧。现有深度学习脑部提取方法通常生成单一固定输出，难以适应不同应用对隐私与解剖保真度的平衡需求。本文提出一种可配置的隐私保护MRI处理流程，通过自适应解剖保留、交互式保留选择与集成质量控制，扩展了基于深度学习的脑部提取能力。该流程使用SynthStrip进行自动脑部提取，再通过形态学掩码扩展生成可配置的壳层保留级别。交互式保留框架允许用户对比不同配置并选择合适输出，集成质量控制框架提供多平面可视化与脑区掩码叠加验证。系统基于Python实现，采用开源神经影像库，并部署于Renku可复现研究环境。在公开的IXI数据集上使用结构化T1加权MRI数据进行评估，实验结果表明该流程实现了解剖合理且可配置的脑部提取，经系统性视觉验证支持。核心贡献为一个模块化、可复现的MRI预处理框架，增强了深度学习脑部提取在隐私导向场景下的灵活性与可控性。该流程为隐私敏感型神经影像研究及协作医学图像分析提供了实用基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。