开发开源工具自动去除医学影像中的患者隐私信息，支持多种影像格式。
De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy
- 集成多种去标识化技术，自动化处理不同医学影像数据。
- 采用神经网络自动清除图像内的文本信息，提升隐私保护效果。
- 适合科研人员快速合规处理医疗影像数据，尤其适用于多模态研究。
用于研究的医疗数据常包含敏感患者健康信息（PHI），受《通用数据保护条例》（GDPR）或《健康保险可携性和责任法案》（HIPAA）等严格法律框架约束。因此，这些数据在使用前必须进行去标识化，这对许多研究人员构成重大挑战。鉴于医学数据种类繁多，需采用多种去标识化方法。为简化医学影像数据的匿名化流程，我们开发了一款开源工具，可对DICOM磁共振图像、计算机断层扫描图像、全切片图像及磁共振Twix原始数据进行去标识化处理。此外，该工具通过神经网络实现图像内文字的自动移除。所提工具可自动化完成多种输入类型的去标识化流程，减少对额外工具的需求。代码已公开，地址为https://github.com/code-lukas/medical_image_deidentification。
原文摘要 · Abstract (English)
Medical data employed in research frequently comprises sensitive patient health information (PHI), which is subject to rigorous legal frameworks such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Consequently, these types of data must be pseudonymized prior to utilisation, which presents a significant challenge for many researchers. Given the vast array of medical data, it is necessary to employ a variety of de-identification techniques. To facilitate the anonymization process for medical imaging data, we have developed an open-source tool that can be used to de-identify DICOM magnetic resonance images, computer tomography images, whole slide images and magnetic resonance twix raw data. Furthermore, the implementation of a neural network enables the removal of text within the images. The proposed tool automates an elaborate anonymization pipeline for multiple types of inputs, reducing the need for additional tools used for de-identification of imaging data. We make our code publicly available at https://github.com/code-lukas/medical_image_deidentification.
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